Forem: Texavor

How to Optimize SEO with Next.js in 2026

Suraj Vishwakarma — Thu, 05 Mar 2026 13:00:11 +0000

TL;DR

Next.js is the most popular React framework. It is being used to build modern web applications. It provides many features that most developers might not use. As the search landscape is changing from traditional search to zero-click search. This shift is driven by Google's AI Overview, ChatGPT, and other AI search platforms. These platforms regularly crawl websites to find metadata and content to index. Content that is easily understandable and crawlable is more likely to be cited and get AI views. Optimizing your webpage for both humans and AI bots is necessary to thrive in this change.

We are going to list down the best practices to optimize the page and content for SEO and GEO.

1. Leveraging the Metadata API for SEO Optimization

Metadata has always been crucial for webpages to state their title and descriptions. Nextj.s replaced the old <Head> tag with its Metadata API. You can add metadata to the Server Component for server-side rendering.

Code Example:

import type { Metadata } from "next";

export const metadata: Metadata = {
  title: {
    template: "%s",
    default:
      "Texavor - GEO & Content Optimization Platform for Writers, Marketers & Developers",
  },
  description:
    "Build your AI content workflow. Discover topics across ChatGPT and Perplexity, and generate data-backed briefs.",
  verification: {
    google: "R53D-JHFSD93JDhjhds_ei99JFADSF", // dummy data
  },
  openGraph: {
    title:
      "Texavor - GEO & Content Optimization Platform for Writers, Marketers & Developers",
    description:
      "Build your AI content workflow. Discover topics across ChatGPT and Perplexity, and generate data-backed briefs.",
    // images: "/easywriteOpenGraph.png",
  },
  twitter: {
    card: "summary_large_image",
    title:
      "Texavor - GEO & Content Optimization Platform for Writers, Marketers & Developers",
    description:
      "Build your AI content workflow. Discover topics across ChatGPT and Perplexity, and generate data-backed briefs.",
    // images: "/easywriteOpenGraph.png",
  },
alternates: {
    canonical: "/",
  },
};

You can add the following:

Title and Description: To define the title and description of the particular
openGraph: This defines how your webpage looks when someone share you website on social media such as Facebook, LinkedIn, and others.
Twitter: Social media preview for Twitter with card type, image, title, and description.
canonical: This points to the original content. This is used when republishing existing content. For SEO purposes, you can mention the current page as canonical, too.
verification: You can add verification tags to verify website ownership with search platforms. This will correspond <meta> tag in the <head> section. You can use this method to verify Google Search Console, Bing Webmaster, Yandex, and others. Learn more about other verification at generateMetadata guide from Next.js.

2. Use Server-Side Rendering (SSR)

Server-side rendering (SSR) is crucial because crawlers prefer fully rendered HTML content immediately. They want the content instantly. With SSR, you avoid showing any loading screen. You can use any rendering method among the following:

SSR: This renders the page on the server for every request and then sends back the HTML with proper data. It is perfect for when the content of the page changes constantly.
SSG: Static Site Generation(SSG) runs at build time to generate the HTML. This generated page is served to the user on each request. It is perfect for pages that rarely change, such as legal pages, about us, and contact us.
ISR: Incremental Static Regeneration(ISR) caches the build-time-generated page and serves it to the request. You can define a revalidate way to re-render the web page after a certain period of time. This is helpful in Blog pages.

export const revalidate = 3600

Apart from SEO, this can help reduce server load and money if you deploy on Vercel.

3. Implement Structured Data (Schema Markup)

Schema markup (also known as structured data) is a piece of code that is added to your website's page that helps search engines and answer engines to understand the meaning of the page. Schema markup can significantly improve the AI visibility in search and AI answers. This makes having schema markup a crucial component of a website. Schema markup has two main formats:

JSON-LD: It is JavaScript Object Notation for Linked Data(JSON-LD). It is implemented as a separate script tag, making it easier to manage, update, and scale
Microdata: It is directly embedded into HTML elements using attributes such as itemscope, itemtype, and itemprop. It makes code less readable and difficult to maintain.

The JSON-LD schema format is preferred by modern webpages.

Code Example:

export default async function Page({ params }) {
  const { id } = await params
  const product = await getProduct(id)

  const jsonLd = {
    '@context': 'https://schema.org',
    '@type': 'Product',
    name: product.name,
    image: product.image,
    description: product.description,
  }

  return (
    <section>
      {/* Add JSON-LD to your page */}
      <script
        type="application/ld+json"
        dangerouslySetInnerHTML={{
          __html: JSON.stringify(jsonLd).replace(/</g, '\\u003c'),
        }}
      />
      {/* ... */}
    </section>
  )
}

Schema markup can be of different types, such as:

FAQ Schema: This is a structured Q&A format that contains commonly asked questions regarding the page or article.
HowTo Schema: It is used for pages that have a step-by-step guide.
Article & Author Schema: It establishes a connection between the content and the author. This strengthens the E-E-A-T(Experience, Expertise, Authoritativeness, and Trustworthiness) signals.

Here are some resources that can help you in mastering the Schema markup:

4. Optimizing Core Vitals

Core Web Vitals are important metrics used by Google to evaluate page experience. They focus on loading speed, visual stability, and interactivity. Here are a few metrics that matter the most:

Largest Contentful Paint(LCP): It measures the time taken to load the largest visible element (card, image, or text block).
Cumulative Layout Shift(CLS): It measures how much the layout unexpectedly shifts during page load.
Interaction to Next Paint(INP): It measures how quickly a webpage responds to a user interaction (clicks, taps, or presses) and updates the content.

Using Next.js Image, we can automatically optimize the large images for LCP.

Code Example:

import Image from "next/image"

<Image
   src={image}
   alt={title}
   fill
/>

Benefits of using the image tag for optimization:

Compression: It automatically compresses the image
Lazy Load: Lazy loading is a technique used to load resources when needed. If the image is not visible, then it won't be loaded, improving loading speed.
Responsiveness: With fill attributes, you can fill the image in the parent container. It supports responsiveness.

Note: When deploying a Next.js application on Vercel, the next/image component uses Vercel’s Image Optimization service, which includes 5,000 image optimization - transformations per month on the free Hobby plan; exceeding this limit may require upgrading to a paid plan for continued optimization.

5. Use Dynamic Sitemap Generation

Sitemaps are essential for crawlers to understand all the crawlable pages available to the bots. This is used by search engine platforms such as Google Search Console and Bing Webmaster to identify all the publicly available pages. You can create a sitemap.ts or sitemap.js file inside the /app route. This will automatically be used for the sitemap. It is a special Route Handler that is cached by default unless it uses a Dynamic API or a dynamic config option.

Code Example:

import { MetadataRoute } from "next";
import { getAllPosts } from "@/lib/posts";

export default async function sitemap(): Promise<MetadataRoute.Sitemap> {
  const staticPages: MetadataRoute.Sitemap = [
    {
      url: "https://www.texavor.com",
      lastModified: new Date(),
      changeFrequency: "daily",
      priority: 1.0,
    },
    {
      url: "https://www.texavor.com/blog",
      lastModified: new Date(),
      changeFrequency: "daily",
      priority: 0.8,
    },
  ];

  const posts = await getAllPosts();
  const postPages: MetadataRoute.Sitemap = posts.map((post: any) => ({
    url: `https://www.texavor.com/blog/${post?.slug}`,
    lastModified: new Date(post?.updated_at),
    changeFrequency: "weekly",
    priority: 0.8,
  }));


  return [...staticPages, ...postPages];
}

You can combine the static pages, like a landing page, about-us, and others, with dynamic ones, such as blog posts. In this way, you can dynamically generate a sitemap in Next.js.

Frequently Asked Question

What are the key SEO benefits of using Next.js?

Next.js improves performance by utilizing rendering methods such as SSR, SSG, and ISR. It also simplifies the dynamic sitemap.

How do I create a custom sitemap in Next.js?

You can create a dynamic sitemap by adding a sitemap.ts or sitemap.js file in the /app directory. It should return an array of URLs using the MetadataRoute.Sitemap API.

How does the Next.js Metadata API help with SEO?

Metadata API in Next.js enables developers to define title, meta description, canonical URLs, Open Graph tags, and verification.

Why is structured data important for SEO and AI search?

Structured data, such as JONS-LD, helps search engines and AI answer platforms, such as ChatGPT, to better understand the web page for accurate citation.

How do Core Web Vitals affect SEO in Next.js websites?

Core Web Vitals measure loading speed, visual stability, and responsiveness, and optimizing them in Next.js using features like next/image, SSR, and optimized assets can improve both user experience and search rankings.

Conclusion

Optimizing SEO in Next.js requires combining strong technical implementation with well-structured content. Features like the Metadata API, flexible rendering strategies (SSR, SSG, and ISR), structured data using JSON-LD, and performance improvements through Core Web Vitals optimization make it easier to build search-friendly applications.

By following these best practices, you can ensure that your Next.js applications remain discoverable, performant, and ready for the next generation of search.

What is AI Visibility? Key Metrics Explained

Suraj Vishwakarma — Tue, 24 Feb 2026 12:30:11 +0000

TL;DR

AI Visibility is a term that refers to how well your brand, product, SaaS, or content is represented and perceived in an AI-driven environment. This environment is majorly dominated by tools like ChatGPT, Gemini, SGE, Perplexity, and Claude. AI visibility will matter more as you search more on the AI search platforms over traditional search engines such as Google and Bing. Even Google has rolled out an AI Overview that results in 58% of searches resulting in zero clicks.

We need to understand the shift in the search to grow brand and content beyond traditional search. This article explores its importance, key metrics, and actionable strategies to optimize your AI Visibility.

What is AI Visibility?

AI Visibility refers to the measure of how prominently and accurately a brand or content appears in AI-powered platforms. Traditionally, ranking top for a specific brand invokes tustibiliyt but today brands and content need to focus on AI-search too. When a user asked query regarding their domain, the content needs to be cited from their website.

Being cited by AI-search platforms impacts brand recognition, customer trust, and further conversations. While click-through citations are fewer, the click is high value and are more likely to convert. Brand and content that get high AI visibiliyt gains competative advantages in trust and recall. Not only the number of citations but also the accuracy of citations matters. Many emerging research has shown inconsistency in AI recommendations. Therefore, consistently tracking your AI visibility and optimizing for it ensures that LLMs cite your brand and content more accurately and reliably.

Key Metrics to Track AI Visibility

Let's look into the key metrics that you should focus on tracking for AI Visibility.

Visibility Score(Share of Voice)

It is a quantitative measure of how frequently your brand, content, or product appears in AI-generated answers for the relevant topics. It provides an overview of how good or bad a brand is visible to the AI.

How it can be calculated:

Number of prompts tested
Number of times your brand is mentioned
High intent queries should matter most and should influence more for the calculation

For example, if your brand appears in 40 out of 100 promotst then your Visibility score(Share of Voice) should be 40%.

Sentiment Analysis

Sentiment analysis is going beyond numbers to the perception of your brand to the AI. It tracks the tone of AI systems dthat escribe your brand when they mention it. It should not mention your brand as cheap or copy of a bigger brand. The sentiment can be categorized into the following based on the response:

Positive: If it mentions your brand as "highly recommended" or "reliable brand."
Neutral: If it just mentions your uses and does not add any opinion on it.
Negative: If it mentions the brand as "Limited features", "Not widely accepted."

Visibility alone is not enough, as sentiment can affect the conversion rate.

Rank and Position

Whenever you asked tools for a particular use case, AI will list down a variety of tools in that domain. Tracking the rank and position of your brand in such a list also becomes important. As users rarely read long AI responses fully. The first 2-3 mentions carry more influence. Based on the AI output order, you can determine the following:

Top Recommendation = strongest influences
mentioned at the bottom = low influence
Included in a sentence with other tools = lowest influence.

Net Sentiment

It is a metric that combines the frequency and tone of the mention. This gives a broader perspective of the AI citations. You can use a formula like the one below:

Net Sentiment = Visibility x Sentiment Weight

Visibility is the number of mentions. Sentiment weights can be calculated by giving points to each citation. You can use the point system below:

Positive: +1

Neutral: 0

Negative: -2

Negative sentiment should have higher weights. For example:

Metric	Brand A	Brand B
Positive Mentions	8	5
Neutral Mentions	2	5
Negative Mentions	2	1
Positive Score (+1)	8 × 1 = +8	5 × 1 = +5
Neutral Score (0)	2 × 0 = 0	5 × 0 = 0
Negative Score (-2)	2 × -2 = -4	1 × -2 = -2
Net Sentiment Score	+4	+3
Total Mentions (Visibility)	12	11

Testing all these metrics on different AI-answer platforms can increase the confident on the score.

How to Improve Your AI Visibility

Once you identify what your visibility is across the different platforms, you can work on it to improve. Let's look into some of the methods that you can apply to improve AI Visibility.

Content Optimization

Optimize content on blogs and docs that are easily parsable and understandable to AI. This can be included by adding the following to articles:

Adding an ordered list over paragraphs
Tables for comparison and statistical analysis
FAQ section to address common questions for query intent
External links to high DA for stats and research
Internal links to all the related articles for topical authority

Technical Aspects

There are technical aspects that you will also need to add to your website. Here is the list of the things

Schema markup: It provides data of the page in JSON-LD format. This makes LLMs/AI understand the website better.
LLMS.txt / llm.txt: These files should be in the root directory. They should cover "What content they can access", attribution preferences, and contact details.
robots.txt: This file should allow bots such as chatgpt-operator, peplexityboy, and google-extended to crawl your site.

You can follow the 7 Steps to Transition from SEO to GEO(AI Visibility) guide for in-depth optimization for maximum AI Visibility.

What are the tools to improve AI Visibility?

You can use the mentioned tools to either measure AI or improve the content for AI Visibility.

Texavor: A platform that does research for topics and outlines based on AI Visibility. I not only analyze the content but also schedule articles to platforms like DEV, Hashnode, Medium, Custom webhook, and others. Making one place to manage all your articles. The content decay system runs on existing articles to alert user for articles that are outdated. Helping users to update articles before they stop getting AI citations and clicks.
PromptMonitor: Runs multiple prompts across different AI-answer platforms to track the Share of voice and sentiments.
Bing Webmaster Tools: You can track AI performance, such as total citations and average cited pages across Microsoft Co-Pilot and partner experiences.

Frequently Asked Questions

What is AI visibility, and why does it matter?

AI visibility is how often AI-answer platforms like ChatGPT, Perplexity, Gemini, and others mention your brand, content, or product. It matters because more and more people are searching ChatGPT.

How do I measure AI visibility effectively?

You can either manually test prompts across different platforms or use a tool such as PromptMonitor to automatically track Share of Voice and Sentiment.

What are the common challenges in improving AI visibility?

Common challenges include technical aspects such as missing schema markup and the llms.txt file. Content-wise, articles are not optimized for AI-answer such as lacking proper structure.

How does sentiment analysis impact AI visibility

Positive sentiment results in better conversions.

Conclusion

AI Visibility is no longer optional - it is becoming a core growth channel. As AI-search platforms like ChatGPT, Gemini, AI Overview, Claude, and Perplexity continue to grow and reshape the search landscape, brands and content must adapts beyound traditonal SEO. Ranking #1 on search engines is no longer enough. If AI systems do not cite your brand — or worse, cite it inaccurately — you lose visibility, authority, and trust at the exact moment users are making decisions. Thus, tracking AI Visibility and optimizing content for content beyond keywords is necessary.

You can use Texavor to optimize content for both humans and AI.

7 Free AI SEO Tools That Boost AI Citations

Suraj Vishwakarma — Thu, 19 Feb 2026 14:30:09 +0000

TL;DR

AI search is increasing rapidly. The content is also evolving to match the shift in search. Generative Engine Optimization might be considered as a marketing tool to sell more subscriptions. But you can't deny that the technical aspects that require LLMs to understand the site are very important and different from SEO.

Today, we are going to look into some of the Free AI SEO tools that could help in improving the technical aspect of the webpage. This will be directly proportional to the citations that you could get through ChatGPT, Gemini, Perplexity, and others.

Website AI Auditor

Is your website ready for the AI era? Check your specialized readiness for Crawlers, RAG, and Entity Understanding.

Texavor's most used free tool is Website AI Auditor. It analyzes the website to find whether your website is crawlable, understandable by LLMs, and readable. If your website isn't crawlable means you are invisible to most of the AI.

It checks the website for the following:

A valid robots.tx that allows AI agents to crawl
Sitemap's presence to find all the internal links
RSS feeds to find the latest content
Content density(text over code)
Valid JSON-LD schema types
Presence of a valid llms.txt or llms-full.txt

Based on this, it provides a rating and grade. You can improve on things that you have missed.

Bing's AI Performance

Track AI performance across Microsoft Co-Pilot and partner experiences

Bing is the only major platform that is showing AI performance as of now. They have launched the AI performance metrics in the Webmaster Tools recently. It shows how your website content is utilized in AI-generated answers across Microsoft Co-Pilot and partner experiences.

It shows the following metrics

Pages cited in AI Answer: It tracks the pages that are being cited in the AI answer.
Average Cited Pages: It tracks the number of unique pages that are being cited per day in AI-generated answers.
Grounding Queries: It is the key phrases that are used by the Answer engine to retrieve content from your site.
Visibility trends over time: You can track the citations and cited pages metrics using a graph over a selected period of time.

Mangools AI Search Grader

See how your brand performs across ChatGPT and leading AI search engines. Analyze your visibility, and competitive strengths across the entire AI landscape.

Check the AI Visibility of your brand across different modeels including ChatGPT, Google, Gemini, and others. By analyzing your brand performance, you can improve on that. It uses a prompt to understand whether you are getting mentioned or not in the answer.

It shows the following results:

score based on the visibility
prompts that are used
Average position of the brand
Visibility of the brand

Content Freshness Checker

Detect content decay signals, find outdated statistics, and get actionable update recommendations to keep your content fresh and SEO-friendly.

Freshness of the content is quite important for citation. This tool checks whether your content is fresh enough for citation. It checks for the following:

Date published and Last Updated date
JSON-LD schema that covers the publish and update date
Days since it was updated
Reference year

This will help in finding content that requires updates. This will help content that is already getting citations to continue getting citations.

Frase GEO Score Checker

Analyze how well your content is optimized for AI platforms like ChatGPT, Perplexity, Claude, and Gemini.

Farse analyzes the given URL and provides scores based on the crawlability and the content of the webpage. It provides three distinct score sections: Authority, Readablity and Structure.

It provides results by analyzing the following:

Meta title, description, and tags
FAQ section, breadcrumbs scheme, and question-based headers
Citations you provide, publication time

It also provides recommendations based on the analysis.

LLMrefs AI Crawlability Checker

Check if AI crawlers can read your page without JavaScript. Test your website's accessibility to AI bots instantly.

Many modern websites use heavy JavaScript that makes AI crawlers struggle to render. You can check whether your website is crawlable by official GPTBot user agents or not. It explicitly checks for the robots.txt to find any blocking. You just need to input the URL for analysis.

It provides the following information:

Content Density
Text length
Semantic HTML
Page type
Schema Types

It also provides recommendations, if any.

Schema Markup Validator

Validate JSON-LD schema markup, check syntax errors, and get GEO optimization recommendations for Article, FAQPage, HowTo schemas.

It does the following:

Track different schema types
Validates JSON-LD schemas
Verify GEO checks such as entity linking, organization/author, geo-friendly types, and others
Outputs all the schemas in a readable code format

Frequently Asked Questions

What are AI SEO tools?

AI SEO tools are used to optimize the content and website for AI Visibility through Generative Engine Optimization.

Are free AI SEO tools effective?

Many free AI SEO tools provide analysis of the website and content with recommendations that you can implement for more AI visibility.

How do AI SEO tools improve citations?

AI SEO tools will analyze the website or content to find gaps and issues. They also provide recommendations that you can work on to improve the AI Visibility.

Can free tools replace paid AI SEO tools?

For a small-scale project, free tools can provide enough information that you can work and start noticing the change. For advanced features, you might need a paid tool.

Conclusion

Free AI SEO tools in 2026 provide marketers, writers, and developers with powerful features to optimize content and boost rankings and citations. By leveraging tools like Texavor's Website AI Auditor, Content freshness checker, and others, you can achieve impactful results.

You can try all the free tools at Texavor here.

What is AI Performance in Bing Webmaster Tools?

Suraj Vishwakarma — Mon, 16 Feb 2026 11:30:16 +0000

TL;DR

Bing has launched the AI performance metric in Bing's Webmaster (similar to Google's Search Console). Bing appears to be the first major search platform to provide citation-level AI visibility data similar to Search Console metrics. This is a cutting-edge feature that provides insight into how your website's content is utilized for AI search.

This article explored its:

Key Features
Comparison between Search Performance and AI Performance
Actionable strategies to optimize the content

Quick Overview: What is AI Performance in Bing Webmaster Tools?

AI Performance in Bing Webmaster Tools shows how your website content is utilized in AI-generated answers across Microsoft Co-Pilot and partner experiences. The partner AI experience includes the following:

Microsoft Co-pilot
AI-generated summaries in Bing
Select Partner AI integrations

It provides data on citations, avg. cited pages, clicks, and others with AI- driven search. This feature is designed to help marketers, developers, and content creators optimize their content for AI-driven search experiences. To see metrics for your website, log in to the Bing Webmaster tool and navigate to the AI performance section. If it will be your first time, then you will need to set up webiste through verification.

Key Features of AI Performance in Bing Webmaster Tools

Previously, we had to use prompts to understand what generative engines think about a brand. It was more like sentiment analysis. There were no tools to measure how the content is being used to cite. It was not possible to track which pages are being cited, what are the queries are being used to cite, and what data to track. We can only track if any user comes to our page through a click from an AI search result. AI Performance in Bing has solved these problems with its tracking metrics. Although it was launched on 10th February 2026, you can track the data from 1st of November 2025.

Let's look into what metrics are being tracked.

Pages cited in AI Answer

It tracks how often a specific page is being cited in an Answer Engine. This can help us to understand the performance of each article. It will help us to evaluate the individual performance of an article. By analyzing this data, we can:

Identify high-performing content
Topics preferred by AI answer systems
Discover patterns in structure, formatting, or content depth
Reverse-engineer successful articles to improve future articles and content

Average Cited Pages

It is the average number of unique pages from your site that were cited per day in AI-generated answers. This will give us insight into how good AI trusts our content. It will also help us to understand how frequently AI systems reference our content. For example:

If your website has 50 total pages
And on average, you are getting 5 unique page citations

That means 10% of your content is being referenced daily. A higher percentage suggests that:

More of your content is being used by AI to answer systems
Your site is considered relevant and useful for answering queries.

Grounding Queries

Grounding queries are the key phrases used by the Answer Engine to retrieve the content from your site to cite. It is not the user queries that are being used, but how AI has parsed the user queries. This will be helpful to identify the user intent over the keywords. It can be based on:

Intent (informational, transactional, comparison, etc)
Context
Sentiment
Semantic meaning

Analyzing grounding queries will help in:

Discovering semantic variation that triggers citation
Optimizing content for meaning, not just for keywords
Understanding how AI interprets user intent

Bing Docs focuses more on the Grounding queries. It may eventually replace traditional keyword metrics in the GEO era.

Additional metrics and features include:

Total Citations: You can get the number for Total citaitons and Average cited pages over a selected period of time
Visibility trends over time: You can track the citations and cited pages metrics using a graph over a selected period of time.
Downloading Data: You can download data for citations, grounding queries, and per-page citations in CSV format for further analysis.

Comparing Search Performance vs AI Performance

Since Bing now provides both Search Performance and AI Performance data, we can compare them over the same time period. This allows us to clearly understand the differences between traditional search results and AI-driven visibility. This comparison is for my technical blogging website, surajon.dev. This comparison is for the last 3 months.

Dataset Overview Comparison

Metric	AI Performance (Answer Engines)	Search Performance (SEO)
Total Queries / Keywords	31	1,598
Average Visibility Metric	20 citations per query	5.5 impressions per keyword
Median	10 citations	1 impression
Maximum Outlier(Maximum number per day)	128 citations	1,089 impressions
Distribution Type	Concentrated	Highly fragmented
Traffic Pattern	Few queries dominate	Long-tail spread

More keywords rank for Search performance is due to the fact that even if your page rank is 60th, it will be given an impression. But with AI performance, it will only link to very few pages that is of high quality.

Distribution Behaviour

Factor	AI Search (Answer Engines)	Traditional SEO (Search Engines)
How many queries drive results?	A small number of queries drive most citations.	A very large number of keywords drive small amounts of impressions each.
Traffic concentration	Traffic is concentrated on a few strong topics.	Traffic is spread across hundreds or thousands of small keywords.
Performance pattern	Some queries get cited many times repeatedly.	Most keywords get 1–2 impressions and very few clicks.
Impact of top performers	Top-performing queries have a very big impact on total AI visibility.	Even top keywords usually don’t dominate overall traffic.
Long-tail effect	Weak long-tail presence. AI focuses more on main intent clusters.	Very strong long-tail presence. Many small variations bring small traffic.
Growth behavior	Growth happens by strengthening topic authority.	Growth happens by ranking for more keyword variations.

This clearly shows how AI search is different from traditional search. Based on the above comparisons of the data, we can clearly state the following:

AI is selective: It will not cite all the phrases/keywords, but only certain topics will be cited repeatedly.
SEO is fragmented: It ranks for multiple keywords but has low individual impact.
Citation concentration exists: AI visibility is not evenly distributed across all your content. Instead, a small number of queries might generate most of your AI citations. It is also due to the fact that I never optimized my content for Generative Engines.
Authority Clustering: Certain topic clusters will likely be more dominant than others.
AI ≠ SEO performance: From the data, we can surely say that high impression does not guarantee AI citations.

Using AI Performance to Improve Your Visibility

Based on our analysis and the docs provided by Bing, we can pinpoint methods that can drive AI citation. This will include how you can use Bing's AI performance to increase citations. Here are the best practices:

Align content with user intent: By reviewing ground queries, you can understand what phrases are driving the citation. This helps us to align our content to the user intent and what kind of content/information supports AI answers.
Strengthen depth and expertise: Content that shows depth and expertise will be cited more. You should cover all the major subtopics that belong to a topic.
Clarity and Structure: Your content should have clarity and proper structure for the heading. Including lists, tables, and FAQ-style content will make the content easier to understand and refreshed by AI.
Links: Whenever you claim something in the content, you should attach a link to that site. This will improve clarity and trust when content is reused in AI-generated answers
Freshness and Accuracy: Content that is fresh, i.e., up-to-date with present data, will be cited more. Accuracy matters as AI will try to minimize the errors and will only pick accurate content.
Content Density over Length: Content that is information dense will be prioritized over that that just use repetative information that is longer.

Frequently Asked Questions

What is AI Performance in Bing Webmaster Tools?

Bing has launched AI performance to track citation, average citaion and grounding queries across Microsoft's Copilot and partner experience.

How does AI Performance help with content optimization?

Tracking AI performance, such as citation, average citaion and grounding query, helps us in identifying patterns and which type of content is cited more often.

Can small websites benefit from AI Performance?

Small websites can take early advantage by utilizing AI performance for tracking citation, average citaion and grounding query. You can target a topic to match the topical authority.

Is AI Performance a replacement for traditional SEO tools?

No, it is another layer of the search. AI Performance focuses on citation, while SEO tools focus on traditional search ranking.

What metrics does AI Performance track?

The following metrics are tracked by the AI performance:

Pages cited in AI Answer
Average Cited Pages
Grounding Queries
Total Citations

Conclusion

Bing's AI performance features mark a significant shift in how we measure visibility in the generative search era. This is the first time that website owners can see how their content is being used to cite in the Answer engine. Understanding the difference between the traditional SEO and AI-driven GEO will allow marketers, developer and content creators to move beyond rankings.

Looking ahead, this is likely just the beginning. As AI-driven search continues to grow, other platforms such as Google, OpenAI, and additional AI-integrated ecosystems may introduce similar citation-level reporting like Bing's AI Performance in Webmaster tools. Websites that adapt early by strengthening topical authority, improving structure, and optimizing for grounding queries will have a strong advantage.

Thanks for reading the article.

References

7 Steps to Transition from SEO to GEO 2026

Suraj Vishwakarma — Thu, 12 Feb 2026 11:30:17 +0000

TL;DR

SEO is not dead yet, but the shift is happening from traditional search engines to answer engines. You don't need to see Gartner's report; you can see for yourself how you are searching today. Next time, when you're writing or updating content, you need to think about AI too.

A well-optimized SEO might get citations and views from LLMs. But fundamentally, there are differences between "How a search engine parses content" vs "How a Generative Engine parses the content".

Today, I will list 7 steps that you could use to transition from SEO to GEO/AEO.

Understanding the Shift: SEO vs AEO

Here are some of the basic differences between the SEO and AEO:

Aspect	SEO	GEO / AEO
Primary Goal	Rank higher on search engine result pages (SERPs)	Provide direct, accurate answers in AI/answer engines
Content Parsing	Page-level indexing and ranking	Passage-level understanding and contextual relevance
Search Query Type	Short-tail (3–5 keywords)	Long-form, intent-driven queries (10+ words, natural language)
Content Strategy	Keyword optimization, backlinks, and domain authority	Structured data, semantic meaning, entity relationships, context depth
Optimization Focus	Title tags, meta descriptions, headings, internal linking	Clear answers, schema markup, FAQs, summaries, machine-readable structure
Content Format Preference	Long-form blogs targeting keyword clusters	Concise, structured answers with sections, bullet points, Q&A format
Traffic Source	Click-based traffic from Google/Bing	Referral traffic from AI engines (ChatGPT, Gemini, Perplexity)
Measurement Metrics	Rankings, CTR, impressions, backlinks	Mentions, citations, AI referrals, and answer inclusion rate
Search Example	`best tool for GEO`	`what are the best tools for GEO for technical writers in 2026`

As we can see from various factors, there are differences between SEO and GEO. This difference can result in not only getting a lower Share of Voice but also incorrect citation or wrong brand placement.

Now, let's look into the 7 steps that you can use to transition from SEO to AEO/GEO.

Step 1: Audit Your Current SEO Strategy

The first step of any strategy is to identify where you stand right now. Address the current SEO strategy and how you are implementing in your website and content. Analyze your website/content for finding these things:

Does your website allow robots to crawl?
Does your website have schema markup?
Is your content targeting clear search intent?
Are your pages internally linked in a logical and crawlable structure?
Are you keyword stuffing too much?

These will give a perspective on where you might be lacking and which direction you need to improve. If you are getting AI-driven traffic from ChatGPT, Perlexity, Gemini, and other platforms, then analyze that content. Analyzing and improving the already cited sites will improve the traffic from them.

Step 2: Implement Advanced Schema Markup

Schema markup is also known as structured data. It is a piece of code that is added to your website's page that helps generative engines to understand the meaning of the page. It is primarily used to provide an entity relationship to the crawler to understand the content. This helps in metrics such as the accuracy of the citation. It consists of properties such as @type , name and subjectOf. Schema markup is believed to improve the citation of a website by 45%. Here are the most important schema markups that you must add to your website:

FAQ Schema: This is the answer and question-format schema. Helpful for AI to answer follow-up questions from the schema and match the user query.
HowToSchema: It is used for step-by-step guide content.
Article & Author Schema: Most important for the article to connect the content and the author. This strengthens the E-E-A-T(Experience, Expertise, Authoritativeness, and Trustworthiness) signals.

Here are the two resources that can help you in mastering the Schema markup:

Step 3: Optimize for AI Context and Reasoning

LLMs use semantic search to find the related text inside the content. It is not going to read the whole article. In SEO, too, it was not reading the whole content, but it was looking for the keywords. But in GEO, the intent of the query should match the content.

Here are the key changes that you could apply to the content/articles:

Completeness: Each passage should be complete in itself. This helps LLMs to get the full context in one passage without requiring additional context.
FAQ section: Adding an FAQ section with a structured list will help match user queries to the content.
Answer with 60 words: Give LLMs a quick answer within the first 60 words so that they can answer the query. You can further elaborate on the answer.
Adding structured lists and tables: Structured rich texts are easy to parse. Thus, adding tables and lists helps LLMs to parse and understand text accurately.

A tool like Texavor can analyze your content to provide insightful tips and metrics that you can use to improve the content.

Step 4: Implement AI-Specific Technical Directives

Generative engines use different crawlers than search engines. For example, chatgpt-operator handles user requests from ChatGPT and perplexitybot indexes websites for inclusion in Perplexity's search results. They mostly look for files such as llm.txt or llms.txt file to find information regarding the website in a plain text format.

Here are technical directives that you can implement on your website:

LLMS.txt / llm.txt: These files should be in the root directory. They should cover "What content they can access", attribution preferences, and contact details.
robots.txt: This file should allow bots such as chatgpt-operator, peplexityboy, and google-extended to crawl your site.
Server Side Rendering Preference: Avoid client-side rendering that can slow the site, and also provide complex JavaScript that AI crawlers may not execute.
Clean HTML Structure: Your content should use a clear hierarchical structure with proper semantic tags. Use proper H2 and H3 for headers and sub-headers.

For llms.txt and robots.txt examples, you can see Texavor's website:

Step 5: Transition from Keywords to Topic

A major transition for content is from keyword to topic. In SEO, we would be writing the whole content around a few keywords with repetition that sometimes feels unnecessary. GEO requires you to address most of the queries that are related to a topic. Simply, it prefers pillar articles. This is more true to Gemini, which prefers longer, detailed guides.

How to implement topic-based GEO:

Pillar Pages: Create 2000+ words when necessary. Avoid increasing word count by using repetitive information. LLMs prefer information density and quality over quantity.
Supporting Cluster Articles: Internally links other supporting cluster articles for better semantic authority.
Query Variations: Try to use a query in the headings to match the question rather than keywords.
Topical Authority: When your content demonstrates topical authority that includes multiple sub-topics in one. It is more likely to cite your content as a primary source of knowledge.

Step 6: Build Trust Signals for AI Systems

Authority is one of the important metrics that AI/LLMs use to cite any website. This is simply used by them to increase their confidence on a particular topic to avoid misinformation. Traditional search engines rely on domain authority through backlinks for authority. AI systems prioritize credibility, consistency, and factual reliability for the authority signal.

How to improve the trust signal for AI/LLMs:

Clear Authorship: Using Article & Author Schema to establish a verified author with the content.
Transparent Sourcing: Mention the article if you are using quotes, state, and outbound links.
Updated Information: Update topics regularly to avoid mentioning outdated data. AI prefers up-to-date content.
Consistency: Consistently publishing high-quality, improved authority.

Step 7: Monitor and Iterate

Similar to SEO, GEO also does not changes over-night. It requires you to do all the good things consistently to see the results. To see any substantial results, it requires at least 3 months. During this time, AI crawls your site and starts citing your content across some related queries.

Here is an industry observation for AI to understand and cite your site.

Phase	What You Might See
2–6 weeks	Early AI mentions & basic visibility
2–4 months	Increased inclusion in AI summaries
3–6 months+	Stable, repeatable citations & traffic impact

During this time, you have to monitor and iterate on the fundamentals of GEO and AEO.

Debunking Common Myths About GEO and AEO

Let's see some of the myths regarding that I find on the internet.

Myth: GEO and AEO are just an extension of SEO

In reality, it is fundamentally different strategies. Both have different goals, such as SEO is for ranking, and GEO/AEO is for citation. Considered SEO and GEO as two separte layer. Both can co-exist, but both work differently for different types of search.

Myth: Keywords are still the primary focus.

Keywords become irrelevant as it never gives the full picture. Modern AI is more focused on the contextual meaning and semantic relevance rather than matching exact keywords.

Myth: Schema markup is optional.

Schema is critical for AI Visibility. It helps in providing structured data about the content that is loved by the LLMs. It gives them more confidence in the content. This results in providing better answers for them.

Frequently Asked Questions

What is the main difference between SEO and GEO?

The main difference is in the goal of the optimization. SEO optimizes content to rank higher in the search engine, while GEO optimizes content to get more citations.

How does schema markup impact AEO?

Schema markup helps search engines to understand the meaning and structure of your content. It makes it easier for AI systems to extract precise answers.

What platforms should I optimize for in 2026?

In 2026, you should optimize your content for the major platform that regularly cites the source. These platforms are ChatGPT, Google Gemini, and Perplexity.

Is keyword optimization still relevant in GEO?

Yes, keyword optimization is still relevant, but its roles have evolved. Rather than focusing on the exact match, you should focus on the semantic relevance and intent.

How can I measure success in AI-driven systems?

You can measure success with metrics such as AI referral traffic, citation frequency, and brand mentions across different platforms.

Conclusion

SEO is not dead, but it's evolving. Traditional search engines still drive the majority of traffic, but the way people discover information is clearly shifting towards AI-driven answer systems. The transition from SEO to GEO/AEO is not about replacing your existing system completely. It requires you add a new optimization layer built for Generative engines.

Websites that adapt early will benefit from increased visibility across ChatGPT, Gemini, Perplexity, and future AI systems. Thanks for reading the article.

References

How I Got Article Views from ChatGPT, Gemini, and Perplexity

Suraj Vishwakarma — Mon, 09 Feb 2026 12:30:10 +0000

TL;DR

I run my technical blog on surajon.dev. I get quite a decent amount of regular views. There are various platforms that drive my traffic, with Google and Bing being the top referrers. I also get views from the Answer Engines. ChatGPT, Gemini, and Perplexity are the only major platforms that drive traffic to my site.

I decided to get insight into the content that gets views from Answer Engines. There are some differences I find among these regarding "How do they cite the articles?" and "What are common themes among them?". Today, we are going to deep dive into those articles and each platform.

So, let's get started.

Analyzing the Articles

I analyzed all my 114 articles against all the articles that have gotten the views from ChatGPT, Gemini, and Perplexity. The past 6 months of the data are only analyzed. I am only considering articles that has atleast 2 views from Answer Engine/AI.

View Rate

Metric	Value
Total Articles Analyzed	114
Articles Getting AI Referers	9 (7.9%)
Articles Getting ZERO Referers	105 (92.1%)

This shows that only 7.9% of the articles are getting AI referrals. This might be the case because these are the articles that were getting more citations, too. This shows that most of the articles are invisible to AI search engines.

The Length Paradox

I can see some differences in the AI getting me the views based on the length. Here is the table describing the difference between viewed and non-viewed based on the article.

Article Type	Avg. Word Count
Viewed Articles	911 words
Non-Viewed Articles	988 words
Difference	-77 words (8% shorter)

As you can see, viewed articles are actually SHORTER. This challenges the common belief that longer content gets more citations. This simply shows that an article that has Quality, strucutre and topic relevance will be cited and get views despite the length of the content.

Platform Distribution

AI Platform	Citations	Avg. Length	Content Preference
ChatGPT	7 articles	~850 words	Curated lists, practical guides
Gemini	2 articles	~1,275 words	Implementation tutorials, code-heavy
Perplexity	2 articles	~820 words	Tool lists, quick solutions

The above results show that each platform has a preference for a certain type of article.

Note: 2 articles were viewd by multiple platforms.

Articles Getting AI Views(All 9)

Multi-Platform Winners:

"Building a Kanban Board with Drag and Drop in React" (ChatGPT, Gemini)

1,637 words
Why: Step-by-step tutorial with code examples

"🚀 20+ Java Open Source Projects to Contribute To" (ChatGPT, Perplexity)

631 words
Why: Curated list format, actionable resources

ChatGPT-Only Views (5 articles):

"5 Best Free Notion Templates for Developers" (665 words)
"4 Best Platforms for Free Web App Deployment" (932 words)
"What are the best platforms to write technical articles?" (866 words)
"Awesome Readme Examples for Writing better Readmes" (649 words)
"How to Build a Custom Video Player in React" (776 words)

Gemini-Only Views (1 article):

"How to Make Money with Technical Writing: A Comprehensive Guide" (1,149 words)

Perplexity-Only Views (1 article):

"Top 5 Tools to Understand Any Codebase Faster" (893 words)

Key Insights

The 7.9% Citation Rate Crisis. If only 1 in 13 articles is getting AI citations, traditional SEO strategies are failing. The data shows AI platforms favor:

Curated lists of individual tool reviews.

Practical tutorials over theoretical explanations
Developer pain points over generic advice
Broader frameworks (React) over niche tools (Supabase).

Tags Most Associated with Citations:

webdev: 100% citation rate among cited articles
react: 60% citation rate
tutorial: 50% citation rate
devtools: 40% citation rate

Tags with Poor Performance:

supabase: 0% citation rate (8 articles published)
css: 5% citation rate
beginners: 10% citation rate

This citation data directly informed the platform-specific strategies outlined below.

We have analyzed the articles in terms of the number. Let's look at the impact and understanding of each platform.

Understanding the AI "Selection Bias"

Each platform has its own way of selecting the article. It can be due to "How users are using different platforms for different kinds of searches?" and the preference for a certain kind of articles that they trust more.

ChatGPT: The "Definitive Guide" Specialist

ChatGPT favors comprehensiveness. It tends to pick articles that provide all the information in one article.

Favorite Format: Definitive guides, "Everything you need to know" pieces.
Avg. Content Length: ~900 words.
Key Signal: Large listicles (e.g., "Top 20+ Resources for X") are favoured. This is also due to it providing rich data to the AI for easy summarization.

Gemini: The "Action-Oriented" Builder

Gemini has a strong preference for a guide and implementation kind of article. It alos prefer articles that deep dive in the article covering a longer length among the others.

Favorite Format: In-depth tutorials and code-heavy "How-to" guides.
Avg. Content Length: ~1,400 words (deep-dive).
Key Signal: High density of code blocks and GIFs. Gemini wants content that solves a specific user problem (e.g., "Building a Kanban Board from Scratch").

Perplexity: The "Resource Hub" Researcher

Perplexity prefers articles that are listicles, providing a list of things. It has the lowest average word count for a view.

Favorite Format: Curated tool lists and external resource hubs.
Avg. Content Length: ~750 words (concise).
Key Signal: High number of external links and specific entity labels (e.g., "Top 5 Tools to Understand Codebases").

The Core AEO Ranking Factors

Despite thier differenct, all are AI that work on retrieving content from webpages. We can improve our content to allow them easy retrieval.

1. Structural Clarity

AI models rely on Retrieval-Augmented Generation (RAG). They are not going to read the whole article; they will retrieve a specific part of the article based on the query.

The Fix: Use strict ## and ### hierarchy. Every section should feel complete in its own way.

2. High Information Density

"Vague" is the enemy of AEO. AI will love the content density and structured content.

The Fix: Use tables for comparisons and bullet points for features. Specificity signals to the AI that your content contains the "raw data" it needs.

3. Visual & Code Richness

For technical queries, "Show, Don't Just Tell" is a ranking factor.

The Fix: Include functional code blocks and descriptive captions for your images. AI crawlers use alt-text and surrounding context to understand if a resource is truly helpful.

4. The "Authority" Signature

AI models are increasingly biased toward first-person expertise.

The Fix: Use "I" and "My" to describe your experiences (e.g., "In my go-to tech stack, I always use..."). This signals AI that it is personal knowledge that can distinguish your content from generic AI articles

Data-Driven Action Plan: From 7.9% to 30%+

Based on the analysis of 114 articles, here's the concrete action plan to improve your AI citation rate:

Stop Doing (What Doesn't Work)

Avoid These Content Types:

Generic beginner's guides that include "How to get Better at Coding."
Providing only explaniation withou any practical application
Writing around a single tool, Write a curated list instead
In Dev, there is a decline in the CSS tutorials and search, too.

The Data Says:

Generic beginner content get 10% citation rate
Framework-specific tutorials like React gets 15% citation rate

Start Doing (What Actually Works)

Priority Content Formats:

Curated Lists (60% citation rate)

"Top 10 X Tools for Y"
"20+ Resources for Z"
Include brief descriptions + links
Target: 600-900 words

Practical Implementation Tutorials (50% citation rate)

"How to Build X with Y"
Step-by-step with code examples
Include CodeSandbox/demo
Target: 1,200-1,600 words

Developer Pain Point Solutions (40% citation rate)

"Best Platforms for X"
"Tools to Solve Y Problem"
Comparison format
Target: 800-1,000 words

Career/Meta Content (33% citation rate)

"How to Make Money with X"
"Best Platforms to Y"
Target: 900-1,200 words

Content Audit Checklist

For each existing article, ask:

Is it a curated list? → High citation potential
Does it solve a specific developer pain point? → Medium-high potential
Is it framework-agnostic or uses React? → Medium potential
Is it niche/framework-specific? → Low potential (consider consolidating)
Is it generic beginner advice? → Very low potential (archive or rewrite)

Frequently Asked Questions

1.Why do only a small percentage of blog articles get cited by AI answer engines?

AI prioritizes content that is highly structured, information-dense, and directly solves a user query. Article lacckcing clear data points are not likely to be picked by RAG-based indexing for AI.

2.Does article length matter for AI citations?

As per our analysis, Article length is less likely to affect citation. Content quality, clarity, and relevance matter the most.

3.What type of content do ChatGPT, Gemini, and Perplexity prefer?

Each platform has a different bias:

ChatGPT favors comprehensive guides and large curated lists
Gemini prefers in-depth, code-heavy implementation tutorials
Perplexity prioritizes concise tool lists and resource hubs

4.How can I optimize my blog posts for Answer Engine Optimization (AEO)?

Use a clear heading structure, high content density, practical code for a tutorial, and first personal signal. These elements can get you more citations.

5.Which tags and topics perform best for AI-driven traffic?

Articles tagged with webdev, react, tutorial, and devtools have higher citation rates for Web Development-specific articles. Although tags are less likely to affect citation, but it can help in better summarization.

Conclusion: Quality Over Quantity, Structure Over Length

The data clearly shows that 92% of the content is invisible to AI search engines. This does not indicate that we have to write more content, but it means we have to write smarter.

Key Takeaways are:

Viewed articles are 8% shorter → Quality beats length
Curated lists outperform → Actionable resources win
Broader frameworks beat niche tools → React > Supabase
Developer pain points get cited → Solve real problems

It represents that the content is most useful and the structure will be more likely to get cited. By adopting this data driven approacch and focusing on the proven content, you can move from 7.9% to the top 30%.

Thanks for reading the article.

Resources

The Role of Blogs in AI-Driven Answer Engine Optimization

Suraj Vishwakarma — Mon, 02 Feb 2026 12:10:16 +0000

TL;DR

Blogs have been crucial to the internet since Web 2, when search was optimized by Google and Yahoo. This makes a website that uses a blog get more visibility. Over the years, businesses and products have used these strategies to attract valuable users to their websites. One of the prime examples, I think, is of LogRocket. They have built a network of good writers who write content for them regularly and drive a significant amount of traffic to them, especially developers. Blogs are still driving major traffic to websites. Websites with a blog get about 55% more visitors than those without one.

Search is also evolving with more people searching through AI to find compile report through the Answer Engine rather than going through multiple links on Google. Even Google implemented Google AI Overview, that resulting into many searches resulting in no clicks on the link. Resulting in zero-click searches. This shows that we are at a time when we need to start focusing on the AI search, too. Today, we are going to look into

Understanding Answer Engine Optimization(AEO).
Role of Blogs in AEO
Methods to improve blogs for AEO

Understanding Answer Engine Optimization (AEO)

Answer Engine Optimization is the process of making changes or writing content to the website/content, so that Answer engines such as ChatGPT, Gemini, Perplexity, Claude, and others are able to understand and cite your website accurately. It is a bit different than Search Engine Optimization, where you fight for a particular keyword for the ranking. While in AEO, you are trying to get cited accurately. This can result in valuable traffic coming to your website. This traffic is more likely to convert than coming from traditional search engines such as Google, Bing, and others.

As mentioned in the introduction, many searches result in zero clicks. This is true for Answer Engine platforms, too. Thus, rather than focusing on metrics like clicks, it is the metric of being cited accurately that defines this era of search. AEO has become a necessary element that require your to focus on it for your brand/content. The future is optimizing content for both to rank in the Search Engine as well as the Answer Engine.

The Role of Blogs in AEO

I mentioned the word "accurately" quite a few times in this article. As the AI is not perfect, not today might not be in the future either, as mistakes can happen. For example, you sell leather travel bags, but your website also mentions:

Not suitable for school backpacks
Unlike laptop bags, this product is made from handmade materials

What goes wrong

A search engine or AI summary may say:

“According to YourBrand.com, the company sells school backpacks and laptop bags.”

Why does this happens

Engine picked the contextual meaning, like school backpacks and laptop bags.
It does not understand the negative words such as Not or Unlike.

This result into wrong product cited. This risk can be reduced by using supporting blog content, as Answer Engines can understand other pages too, and actually find exactly what you sell and how you sell. This provides more contextual explanation about your brand. When cited accurately, increases the likelihood of users visiting your website directly to make a purchase.

The traditional article writing needs to be reworked in some ways so that answer engines have data to accurately cite. This can be done using the following:

Using Schema Markup
Content Structuring for AEO
Content Freshness

We are going to look into each of these methods.

Leveraging Schema Markup for Blog Optimization

Schema markup, also known as structured data, is a piece of code added to your website's page that maps an entity with its relationship. This is done by using entities such as @type , @name , and subjectOf. Schema markup implementation can improve the citation of the website. Some blogs claim that it has increased the citation of the website by 45%. This is due to the fact that schema markup helps answer engine to understand the webpage/content accurately. This avoids AI hallucination, thus giving a good signal that they are confident enough in the content to cite.

Primarily for Answer Engine Optimization, we use the following Schema Markup.

FAQ Schema: This is a structured Q&A format that contains commonly asked questions regarding the page or article.
HowTo Schema: It is used for pages that require step-by-step instructions that AI can cite as a guide.
Article & Author Schema: It establishes a connection between the content and the author. This strengthens the E-E-A-T(Experience, Expertise, Authoritativeness, and Trustworthiness) signals.

Here is an example of the FAQ Schema.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is schema markup?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Schema markup is structured data that helps search engines and AI tools understand webpage content."
      }
    },
    {
      "@type": "Question",
      "name": "Does schema help with AI search results?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes, schema improves content extraction and citation in AI-powered answer engines like ChatGPT and Perplexity."
      }
    }
  ]
}
</script>

Resources to learn more about Schema markup and how to implement.

How to Build Schema Markup for AEO: A Step-by-Step Guide: Detailed guide on how to implement schema markup on your website and best practices.
AEO Schema Validator: Schema Validator, especially for the AEO.
Google Rich Results Tes / Schema.org Validator: For validating the syntax of the schema markup.

NLP and Content Structuring for Answer Engines

NLP stands for Natural Language Processing. It is the mind behind the Answer Engine that utilizes to take the query, parses it, removes grammar errors, understands the context, and generates the output. If your content is easy to parse and understand by NLP, they can cite you more often than competitors.

Writing content with a human first is still the best practice as AI is designed to understand human but the content also needs to help NLP to easily understand the sentences and paragraphs with correct context. Here are some of the best practices that you deploy while writing content:

Match Natural Language: For SEO, we were writing around keywords to rank, but for AEO, we need to match the user intent and write in more natural language.
Markup Schema: Using the correct schema markup type Article for general Article and TechArticle for Technical Article. Link the content to the author in schema markup, which helps with authority.
FAQ Section: Adding one FAQ section either after the conclusion or before, so that it can serve as the primary query or follow-up question asked by the user in Answer Engine.
Internal Link and Topic Cluster: Add internal links to related pages and articles to show topical authority. Write content around one central topic and add links to the supporting posts.

Content Freshness and Its Impact on AEO

Traditional search engines used to rank articles that were written 2-3 years back on a certain keyword by using proper techniques. You might have seen content written in 2023, but changing the title to include 2026 ranks still higher. But answer engines are going to pick content that is fresh and accurate in today's time. Thus, Content Freshness is one of the key metrics that will be used by AEO to cite.

Tools that provide Freshness Guard for content written previously will help in finding content that requires an update. It can provide a score that can represent the following:

🟢 100 (Fresh): Published recently or is an Evergreen topic. No action needed.
🟡 80 (Evergreen): Older date, but the topic is stable. Safe.
🔴 0-40 (Decayed): High-risk topic + Old date. Required Update.

Using such a tool, you can find the article/content that require update so that AI will cite or continue to cite.

Best practices for updating the blog content:

Mention "Last Updated" Signal: Displaying the last updated helps Answer engines to cite accurate information.
Refresh Examples, Screenshots, and Data: Update the content with the updated screenshots and code block as per the latest changes.
Adding Context: Add an FAQ section, use better visuals like tables for comparison, and use definitions if you missed them for old articles.

FAQ

1. What is Answer Engine Optimization (AEO)?

AEO is the practice of optimizing content to be directly surfaced as answers by AI-powered search engines.

2. How do blogs contribute to AEO?

Blog provide accurate, structured, authoritative content that aligns with the AI-driven engines.

3. What tools can help optimize blogs for AEO?

There are various tools that you can use to help you optimize content for AEO and GEO. Texavor combines AI Visibility through blogs that help you providing AI Visibility score for research. Freshness Guard for articles and integration with popular sites for managing the articles on different platforms.

4. Is AEO replacing SEO, or do they work together?

AEO is not going to replace SEO - they are going to work together. SEO focuses on ranking in search engines, while AEO will focus on being cited accurately on answer engines. The most effective strategy is to optimize the content for both.

5: How can I tell if my blog is being cited by answer engines?

You can check AI citations manually by quering through different platforms such as ChatGPT, Gemini, Perplexity, Claude, and others.

Conclusion

As the search is evolving, the content also evolves. Blogs have become a cornerstone for driving AI traffic to the platform. This can be achieved through structured data by implementing Schema Markup, FAQ, and regular updates. This ensures that content remains relevant and visible in the evolving landscape of the answer engine.

You can audit your existing blog to implement:

Basic Schema markup for key posts
Restructure content for clarity and intent, not only for keywords
Update the old content to keep it fresh

By applying these steps, your blog becomes easier for answer engines to understand, trust, and cite - while remaining genuinely useful for human readers. Thanks for reading.

How to Build Schema Markup for AEO: A Step-by-Step Guide

Suraj Vishwakarma — Thu, 29 Jan 2026 11:58:34 +0000

TL;DR

Schema markup is the essential component for Answer Engine Optimization(AEO). A perfect schema markup helps chatbots and generative engines like ChatGPT, Gemini, Perplexity, Claude, and others to understand your website accurately. This schema markup is also used by traditional search engines such as Google and Bing. In this way, using schema markup, we can optimize our website for both SEO and AEO.

In this guide, we are going to look into the following:

What is Schema Markup?
What are the types of Schema Markup necessary for AEO?
Step-by-Step Guide to Building Schema Markup
Whare are the Best Practices for Schema Markup in AEO?
How to choose the right schema type for your content?

So, let's get started.

Understanding Schema Markup and Its Role in AEO

Schema markup (also known as structured data) is a piece of code that is added to your website's page that helps search engines and answer engines to understand the meaning of the page. Schema markup does not increase the rank for a traditional website, but it helps answer engine/search engines to understand the purpose and content of the webpage accurately. Schema markup uses a standardized pattern defined by schema.org. Schema markup consists of different properties, such as @type, name, and subjectOf, which describe an entity and its relationships. Having schema markup strengthens the authority and trust signals for the answer engines.

Schema markup implementation can improve the citation of a website by 45%. It also helps search engines to cite your product/website accurately for the user query. Accurate information will increase the chance of conversion when they visit the website. Perplexity especially relies on the entity graphs, not just URLs.

Types of Schema Markup for AEO

Schema markup can have multiple types based on the purpose. Here are some of the major types of schema markup:

Organization Schema: Establishing your business/website with the correct name, logo, contact info, social profile, and corporate details.
LocalBusiness Schema: This is particularly for the businesses that has physical store. This can include entities such as hours of operation, address, phone, service areas, and any relevant location-specific information.
Product Schema: This is for when you have a physical product that can be associated with an entity, such as pricing, availability, reviews, SKUs, and any ongoing offers.
FAQ Schema: This is a structured Q&A format that contains commonly asked questions regarding the page or article.
HowTo Schema: It is used for pages that require step-by-step instructions that AI can cite as a guide.
Article & Author Schema: It establishes a connection between the content and the author. This strengthens the E-E-A-T(Experience, Expertise, Authoritativeness, and Trustworthiness) signals.

Among these, we are going to focus primarily on the FAQ, HowTo, and Article & Author schema. These schemas are the most important to the Answer engine for citation.

How to Choose the right schema type for your content

Choosing the correct schema type isn't about adding more markup but accurately describing what the content is so search engine and answer engine tools can understand, trust, and cite it accurately. Below is the table define which type of Schema you can choose to describe each page.

Page purpose	Primary schema
Blog or technical post	`Article` / `TechArticle`
Step-by-step guide	`HowTo`
Q&A content	`FAQPage`
SaaS / tool page	`SoftwareApplication`
Product or pricing page	`Product`
Company info	`Organization`
Author bio	`Person`

Step-by-Step Guide to Building Schema Markup

Let's look into the details step-by-step to set up the Markup on your webpage. We are also going to look at "How to set up Markup schema in Nextjs?"

Step 1: Identify the Content Type and Goals

First, identify what the content of the page represents. It can be representing aritcle, a product, a blog, an FAQ, a tool, and other. Then decide the goal, whether it is for rich content, AI Visibility, or information.

Step 2: Choose the Appropriate Schema Type

After identifying the type of content, we can now match it to the correct schema type. As we discussed previously different schema types that you can choose to most accurately represent your schema type.

You can also use a tool such as:

Google's Structured Data Markup Helper
Visual Schema builders or schema generator

to identify the schema.

Step 3: Generate and Validate the Schema

Schema markup has two main formats:

JSON-LD: It is JavaScript Object Notation for Linked Data(JSON-LD). It is implemented as a separate script tag, making it easier to manage, update, and scale
Microdata: It is directly embedded into HTML elements using attributes such as itemscope, itemtype, and itemprop. It makes code less readable and difficult to maintain.

Here is a key difference between them.

Feature	Microdata	JSON-LD
Location	Inside HTML tags	Separate `<script>`
Readability	Low	High
Maintenance	Hard	Easy
React / Next.js	❌ Poor	✅ Excellent
Google recommendation	❌	✅
AEO / AI tools	❌ Weak	✅ Strong

As you can see, AEO/AI tools prefer JSON-LD format; we are going to use that format when creating our schema markup onward in this article.

Below is the example schema markup in JSON-LD format for different types of schema that you understand and implement in your website, as per your page.

TechArticle Schema

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "headline": "How to Implement Schema Markup for AEO",
  "description": "A step-by-step guide to creating and validating schema markup for AI-driven search engines.",
  "author": {
    "@type": "Person",
    "name": "John Doe"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Example SaaS",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png"
    }
  },
  "datePublished": "2025-01-10",
  "dateModified": "2025-01-15",
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://example.com/schema-markup-guide"
  }
}
</script>

This lets AI tools understand:

What the content is
Who wrote it
Who published it
Where it lives

FAQ Schema

Use only if FAQs are visible on the page.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is schema markup?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Schema markup is structured data that helps search engines and AI tools understand webpage content."
      }
    },
    {
      "@type": "Question",
      "name": "Does schema help with AI search results?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes, schema improves content extraction and citation in AI-powered answer engines like ChatGPT and Perplexity."
      }
    }
  ]
}
</script>

You can use Texavor's Free FAQ Schema Generator that can help you in creating FAQ schema through QnA and also with URL that can pull FAQs from website and articles to create the schema.

HowTo Schema

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to Add Schema Markup to a Website",
  "description": "A step-by-step guide to creating and implementing schema markup using JSON-LD.",
  "totalTime": "PT30M",
  "supply": [
    {
      "@type": "HowToSupply",
      "name": "Text editor"
    }
  ],
  "tool": [
    {
      "@type": "HowToTool",
      "name": "Schema.org Validator"
    },
    {
      "@type": "HowToTool",
      "name": "Google Rich Results Test"
    }
  ],
  "step": [
    {
      "@type": "HowToStep",
      "name": "Identify the content type",
      "text": "Determine whether the page is an article, product, FAQ, or guide before creating schema."
    },
    {
      "@type": "HowToStep",
      "name": "Create JSON-LD schema",
      "text": "Write schema markup in JSON-LD format using the appropriate Schema.org type."
    },
    {
      "@type": "HowToStep",
      "name": "Validate the schema",
      "text": "Test the markup using Schema.org Validator and Google Rich Results Test."
    },
    {
      "@type": "HowToStep",
      "name": "Implement on the website",
      "text": "Embed the validated schema into the HTML or CMS of your website."
    }
  ]
}
</script>

This tells AI about what the instruction is for, the name, description, tool, and steps to follow.

Here are some of the common mistakes to avoid when writing schema:

Using the FAQ Schema without having a visual FAQ section
Mismatch between the content and the schema
Forgeting publisher and author for Article/TechArticle Schema\
Adding multiple primary schema types

You can validate the schema through the following:

Google Rich Results Test: Copy-paste the schema or provide the URL.
Schema.org Validator: Here, too, you can either copy-paste the code snippet or provide a live URL.

You can also use the above tools for creating schema visually.

Step 4: Implement the Schema on your website

You can now embed the validated schema into the <head> or <body> of your HTML. If you are using any CMS like Wordpress they provide ways to add code snippets into the Head section of the HTML.

For NextJS, you can follow a JSON-LD implementation. Below is the code for the NextJS implementation. You can create the schema in the page.tsx file of any route.

export default async function Page({ params }) {
  const { id } = await params
  const product = await getProduct(id)

  const jsonLd = {
    '@context': 'https://schema.org',
    '@type': 'Product',
    name: product.name,
    image: product.image,
    description: product.description,
  }

  return (
    <section>
      {/* Add JSON-LD to your page */}
      <script
        type="application/ld+json"
        dangerouslySetInnerHTML={{
          __html: JSON.stringify(jsonLd).replace(/</g, '\\u003c'),
        }}
      />
      {/* ... */}
    </section>
  )
}

Step 5: Test, Monitor, and Optimize

After successful deployment, you can test the schema through with URL to verify correct schema implementation. You should monitor the schema for enhancements, errors, and updates.

Best Practices for Schema Markup in AEO

Here are some of the best practices that you should follow for better AEO schema markup:

Accurate and Up-to-Date: Make sure the schema markup is accurate to the content. You should also keep updating the schema as per changes in the content. This increases the understanding and trust in answer engines.
Structure Content for Answer Extraction: Focus on user intent for the schema choice and selection for AEO. You should optimize the schema with search queries.
Avoid Common Mistakes: We have already discussed the common mistakes for schema markup. Try to minimize the mistakes as much as possible.
Avoid Schema Overload: More Schema does not result in better citations. Keep it clean, minimal, and accurate to the content.

Conclusion

By implementing schema markup effectively, it is more likely that your content will be cited by Answer Engines. Use the tools and steps outlined in this guide to stay ahead in the evolving landscape of Answer Engine Optimization.

Thanks for reading the article.

Future Trends in Answer Engine Optimization: What to Expect in 2026

Suraj Vishwakarma — Mon, 26 Jan 2026 11:30:14 +0000

TL;DR

From the initial skepticism about whether the landscape of search will change, today we are standing where one of the focus areas for brand visibility is AI. This year's expectation is that AI search is going to increase in numbers. It is predicted by Garnter that by the end of 2026, websites will see a 25% reduction in organic traffic. Search is not reducing, but search is shifting towards Generative AI like ChatGPT, Gemini, Perplexity, and others.

Answer Engine Optimization(AEO) has become a necessary element that require your to focus on it for your brand/content.

In this article, we are going to look into:

What to expect from Answer Engine Optimization in 2026?
What will be the trends in Answer Engine Optimization for 2026?

So, let's get started

What is Answer Engine Optimization (AEO) and Why It Matters in 2026?

Answer Engine Optimization(AEO from now onwards) or Generative Engine Optimization(GEO from now onwards) can be used interchangeably, as they both represent optimizing the content that results in getting cited by major generative AI when a query is asked in your domain. It will play as the SEO that you have worked to make your website rank higher for a particular keyword. AEO, on the other hand, rather than focusing on the keyword, will focus on intent and freshness of the content.

As Google rolled out the AI Overview feature, page views on the top-ranked pages were slashed by 30%. This has influenced the user to see and trust AI more often, as these LLMs (Large Language Models, technology behind Generative AI) are getting better at citing and extracting the relevant and accurate information. Also, viewer coming from Generative AI referrers are more likely to convert as they already gather enough information to choose your brand/platform.

Now, it has become necessary to focus on AEO along with SEO in 2026, as these two will coexist with each other in the coming years. Let's look into some of the major trends that will reshape the Answer Engine Optimization in 2026.

1. The Role of Structured Data in AEO

As we have discussed, traditional SEO focuses on the keyword, but the AEO focuses on intent. Intent is the understanding of the LLM regarding "What the user wants?", it can be a definition, a recommendation, an understanding, and others. Example: "best phone under 30k" → intent is recommendation, not definition. To get the full intent, the AI engine will not just focus on the keyword they will require more data to understand whether your product/brand matches the user intent. Due to this, it has become important that AI understands your product/brand accurately.

Keywords can be embedded in multiple places, but for complete data, you need a structured format so that the AI can understand and cite you. Your website/content will be the sole place of the truth, so it should be optimized for the AI engines. One of the best ways to improve the structured data for AEO is to improve the schema markup of your website. Schema markup helps machines(LLMs) understand your content accurately.

2. Conversational AI and Voice Search Optimization

All the major AI Generative engine provides voice search with AI, and also other Voice search, such as Alexa and Google Mini, will integrate AI to provide better and up-to-date information. Conversational AI involves human langauge query, contextual, and follow-up questions thatthe user can ask. As a user, you can have multiple questions regarding one simple query, so understanding the user's question and optimizing content for that will become important.

This optimization will be more relevant to AI Assistants and Mobile devices. 20.5% of the global population regularly uses voice search in 2026, according to AICurator. Thus, adapting your content for spoken for better brand visibility and impression will also be part of AEO.

3. Authority and Source Credibility

Generative AI will look at your website for credibility and authority in the domain to cite you. This is due to LLMs are going to become more accurate, which will lead them to only extract information from sources that are safe, accurate, and trustworthy enough to cite or synthesize into a direct answer. By 2026, answer engines will prioritize source-level trust over page-level optimization. This means content is evaluated not only on relevance, but also on the reliability of the entity publishing it.

Why Authority Matters More in AEO Than SEO?

The answer engine operates under high risk model. Incorrect information provided by them will hamper their own credibility. Thus, they will prefer content that demonstrates:

Consistent information across different sources
Clear authorship
Alignment of the content with widely accepted information

If an answer engine cannot confidently attribute information to a credible source, that content is most likely excluded - regardless of how good the keyword optimization is.

Actionable Strategies for Marketers, Developers, and Writers

Marketers have to acknowledge the change in the search to devise better strategies for AEO content. The shift has to start from tailoring content to make a balance for both the SEO and AEO for better ranking and citing from the Answer Engine. They can not simply ignore AEO and focus on SEO and hope that their content is also cited by Answer Engine without optimizing for it.

Developers need to implement proper schema markup, an accurate sitemap, maintaining proper robots.txt. They also need to add features not only for the User but also for the bots and search engines.

Writers need to write not only for the keyword but also for thinking as the user and what queries the user can arrive at. Adding FAQs sections in the articles to address commonly asked questions by the user.

You can run a free test of your website to find what you are missing that is essential for the Answer engine to crawl your website from a technical point of view. Run a free Website AI Auditor test at texavor.

FAQ

What is the difference between SEO and AEO?

SEO focuses on ranking the website on search engines such as Google and Bing, while AEO focuses on getting your content/product/brand cited by Answer Engine like ChatGPT, Gemini, Perplexity, and others.

How does Answer Engine Optimization (AEO) impact content creation?

Today, content creator does not only write content by stuffing keywords but also think about queries that user can ask to answer the engine.

What tools can help with AEO and GEO?

There are various tools that you can use to help you optimize content for AEO and GEO. Texavor, helps you in researching topics and content that are more likely represent your Authority and also that are more likely to cite by Answer Engine. It also helps you in organizing and managing content on different platforms using a scheduling tool.

Conclusion

The digital landscape is shifting towards an AI-first discovery and answer engine. By adopting strategies like AEO, structured data, conversational AI, and authority, marketers, developers, and writers can stay ahead of the curve. This will ensure that your content remains discoverable and impactful in 2026.

How Texavor Helps You Rank in ChatGPT, Perplexity, and AI Search Results

Suraj Vishwakarma — Thu, 22 Jan 2026 11:30:06 +0000

TLDR;

Search is ever evolving with the rise of generative AI, as you can see the integration of these AI in most applications. You don't need to read different surveys or reports; you can see the pattern in yourself. "How do you search today?" For marketers, developers, and blog writers, ranking in this system requires a different approach than traditional SEO.

Texavor is a platform that focuses on AI Visibility and Authority for your blog. It is designed for research-oriented topics and keywords that can help in ranking on these cutting-edge platforms. In this article, we will explore how Texavor helps you achieve better rankings in AI search results.

So, let's get started.

Understanding AI Search Engines: ChatGPT, Perplexity, and Beyond

AI models analyze the meaning behind a query, not just keywords. Example: "best phone under 30k" → intent is recommendation, not definition. Due to this, we need to have a proper understanding of what the user can ask. NLP(Natural Language Processing) is the mind behind all the generative engines. Its main focus is to understand the user request, not the words that they are using in the query. Due to this, they will treat "cheap", "budget", and "affordable" as related concepts.

Ranking in this search engine can enable your brand to have more and better impressions. It is more likely that a user coming from the AI Engine to convert than from the traditional search. Also, Product mentions by Search eninge can increase the credibility of the brand. Various things can easily benefit your website/product by just optimizing your content for the search engine.

AI Search engine is growing bigger and bigger day by day. It is too hard to ignore this source of impression. Here, Texavor comes in to help you improve the AI visibility of your website through blogs.

Core Features of Texavor That Enhance AI Search Rankings

Both search engines and an AI Search engine are going to punish articles/blogs that purely use AI to write the article. Texavor doesn't write an article for itself, but provides a suite of tools that you can use to manage, research, collaborate, and analyze blogs. We are going to look into some of the features that can help you improve AI Visibility of your website.

Keyword Discovery and Research

Texavor analyzes your website and blogs to identify high-potential keywords that you can use to write the article. Keyword Discovery provides relevance, authority, and opportunity score. The main score is the AI Visibility score that indicates how good the keyword is for an AI search engine. Writing an article around that can increase the chances that the article will be cited by the AI search engines.

Keyword Research is designed to help find keywords through your own search. You can search for different words, sentence or groups of related words to find stuructured keyword from those. Using this will refine your idea into a structured shape. You can search through different sources for better results. Here are the 3 sources that Texavor uses:

Basic: It uses Google's AutoComplete to extract potential keywords
Detailed: It uses DataForSEO for finding high-potential keywords. It provides more metrics to understand different keywords
Prompt: It is designed for AI visibility; it will provide keywords that are more likely to be cited by search engines.

Note: Initially, I mentioned intent is the key for the AI search, but we are using Keyword research. Keywords are still important as AI search engines still rely on the language patterns.

Topic Generation

It is the tool that converts the keywords/group of keywords into a meaningful topic/title of the article with a meta description. It generates 7 topics per query. It provides a detailed review of each topic, focusing on why this Topic a unique approach that you can take to write an article, the difficulty, and the opportunity.

It will also provide an analysis of the search query in terms of opportunity, difficulty, geo score, and top competitors' articles.

Outline Generation

The topic/title is now converted into a structured outline through outline generation. In this, we can enable the AI Optimized for AI toggle to improve the AI Search engine. An outline is necessary as we don't write the article for you, but we can use an outine can you help you write the article to cover the topics and things that are necessary or missed by the competitors.

The outline generated is editable; you can add or remove sections with key points. You have total control over the outline.

Other Features

There are other features that you would love to explore on the platform. Here are some of them:

Article Management: You can manage, create, write, and analyze the article through Article features.
Competitor Analysis: Analyze your competitors through RSS feed or sitemap to understand and extract topics that you can write an article on.
Platform Support: Texavor supports platform integration for Medium, Devto, Hashnode, WordPress, Webflow, Shopify, and Custom Webhook to publish and schedule articles.
Thumbnail Styles: Analyze your website to generate thumbnails for each article that suits your style.

You can explore Texavor to find other features that will let you improve your workflow for blog management that focuses on AI Visibility and Authority. You can learn more about it at Texavor and Texavor Docs.

The Role of Contextual Relevance and Freshness in AI Search Rankings

Contextual relevance is how well a piece of content matches the full intent, situation, and constraints of the user's question. Users can ask multiple queries one after another, so the content must be relevant and contextually aware - not just popular web pages. For example: “Best backend framework for startups in 2026” requires recent context, not a 2022 article. An AI search engine prefers content that is:

Clearly defines the problem
Matches the exact use case
Answer the question before expanding

While you also see many articles that were written in 2022, only the title changed to 2026, ranked quite high on the Search Engine, but the AI Search engine prioritizes fresh content. Fresh Content signals accuracy, reliability, and relevance of the information.

Texavor, can check your previous articles to find decay content that might require an update so that the AI Search engine can continue or cite your article.

Future Trends in AI Search Optimization and How Texavor Prepares You

As the search engine gets stronger with context and information, it is more likely to dominate the search in the coming days. They are more likely to cite your site if you have fresh content that matches the intent.

Texavor is built specifically for AI-first search environments. It will provide you with key information that can help in taking decision that can help you grow.

Conclusion

AI search is no longer an experimental layer on top of the traditional search. It is becoming a primary way people discover, compare, and decide. Ranking is no longer just about keywords or backlinks; it's about contextual relevance, freshness, and authority.

Texavor is built for this exact shift. Instead of replacing human creativity with AI-generated content, it empowers creators, marketers, and developers with the right signals, insights, and structure needed to be visible in AI-driven search engines. With Texavor, you’re not just optimizing for today’s search—you’re preparing your content strategy for the future of how information is found, understood, and reused by AI.

I hope this article has helped you know understading How Texavor Helps You Rank in ChatGPT, Perplexity, and AI Search Results. Thanks for reading the article.

How I Grew My New Blog to 4,000 Visits in 30 Days

Suraj Vishwakarma — Mon, 10 Nov 2025 11:30:00 +0000

Introduction

I used to run a blog on surajondev.com, but it was WordPress-hosted. As the blog grows, I need more control over the design, structure, and analytics, but WordPress is limited. So I let that domain expire and deleted the blog(I should have preferred to migrate; that was my mistake ), thinking that I would buy the same domain from Namecheap and continue, but even after waiting for 6 months. I couldn’t get the domain to list.

So, I dumped the idea of buying the same domain. I purchased the surajon.dev. I seriously started posting articles a few months ago. I started posting new articles and previously published articles from WordPress. Not only did I just copy and paste the content, but I also improved it for today. In a month, I was able to grow the blog to 4K visits. Below you can see the details of the analytics.

Today, I am going to list down the methods that I used to increase blog growth in one month.

SEO Optimization

I primarily connect to Google and Bing search engines. From there, after the scan, this platform provides you with a bunch of suggestions that you can start implementing on your website. You can also run a manual scan from Site Scan on Bing.

I also used the Ahrefs free version to identify further issues in my website. I tried to fix all those. I was able to get more impressions and a better position now.

Some of the fixes include:

Optimized title within 50-60 characters.
Optimized description within 150-160 characters.

I worked on optimizing the title and description of every article. It is not only for search engines, but to increase your Click-Through Rate(CTR). A good title and description make people want to click your link over others in the search results.

You can audit your website from Ahrefs to find SEO issues that you can work on it. You can learn more about technical SEO through my article, The Developer's No-Fluff Guide to Technical SEO.

Cross Posting on Blog Platforms

I have a blog on dev.to, dev.to/surajondev, where I cross-post my blog articles here. I provided the canonical URL for my website’s article. With a canonical URL, the search engine will understand that the original article is from the website.

I noticed that after cross-posting to dev.to, my original article started appearing in the Google Discovery feed on mobile, which brought in a new wave of traffic I wasn't expecting for a new website.

Link Sharing

Sharing your article links on various websites not only gets you reach but also builds a solid backlink. I started using X to share the article and through my newsletter on Substack.

I would also recommend sharing your post daily.dev](https://app.daily.dev/) if it’s focused on developers or designers. You can get a good amount of views from there.

Note: I share my links on X and developer communities like daily.dev. The key is to share high-quality content that is genuinely helpful, not just spamming links, which can hurt your credibility.

Conclusion

These are the methods that I used to grow views to my blog in the last month. My next target will be to reduce the bounce rate. While in the case of a blog, a bounce rate being higher is fine, as people come, read the article, and leave. But reducing it will help the blog with internal links and more discovery.

Thanks for reading the newsletter. What growth tactics have worked for you? I'd love to hear them.

What is IndexNow, and How Do I Set It Up on a Website?

Suraj Vishwakarma — Wed, 05 Nov 2025 12:23:05 +0000

Introduction

As a technical blogger who hosts their own blog, one of the most frustrating things is that Google and Bing's crawlers take their own time to crawl and index the latest articles. They mention that they will crawl daily, but often they don't, and new articles don't get indexed instantly.

Initially, when I published content on my websites, surajon.dev and easywrite.dev, the articles were visible on the site, but there was a delay before they were indexed by search engines. This can be detrimental, as you might miss out on trending topics, or the page might not get indexed at all.

That's where IndexNow comes in. It's a simple protocol that has fundamentally changed my publishing workflow. Instead of waiting for search engines to come to me, I now tell them the instant a new article is live.

Here’s a breakdown of what it is, why you need it, and how I implemented it.

What is IndexNow?

IndexNow is an open-source protocol that lets website owners instantly notify search engines about new or updated content. It replaces the old method of waiting for a crawler like Googlebot to visit our site to find new pages. With IndexNow, you send a ping request to an IndexNow API endpoint. This simple ping tells the search engine that there is a new page on your website. Search engines that support the protocol receive this ping and add the URL to their priority crawl queue. This is a push system.

It was originally developed by Microsoft Bing and Yandex. Here is the list of search engines supporting IndexNow:

Microsoft Bing
Yandex
Yep
Amazon Bot

See the complete list of search engines supporting IndexNow.

While Google does not officially support it, they have tested it in the past and might implement it in the future. By setting up IndexNow, you might also benefit from Google's support in the future.

Setting Up IndexNow

Setting up IndexNow can be divided into three steps:

Step 1: Generate a Host API Key
First, we need to generate a unique API key. You can generate one from Bing’s IndexNow Page.

Step 2: Add the Key to Your Public Directory
Next, create a text file named after your API key (e.g., 1dbae54107asdfadsb791cf94132d24bdc0.txt). This file must contain only the key itself:

1dbae541077243b79adfadsf132d24bdc0

Placing this file in your website's root or public directory makes it accessible at a URL like https://www.example.com/1dbae541077243b791cf94132d24bdc0.txt, which is required for verification.

Step 3: Send the Ping
Whenever you publish new content, you need to make an API call to https://api.indexnow.org/indexnow with a list of the newly added URLs. You can also send a complete list of your site's URLs, including the new one.

Here is the curl:

    curl -X POST "https://api.indexnow.org/indexnow" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d '{
      "host": "www.surajon.dev",
      "key": "3f2f5c8c4f8adfadf8c4f8c4f5c8c4f8c",
      "keyLocation": "https://surajon.dev/3f2f5c8cadsfads4f5c8c4f8c4f5c8c4f8c.txt",
      "urlList": [
        "https://surajon.dev/blog/new-react-hooks-guide",
        "https://surajon.dev/blog/updated-adonisjs-tutorial"
      ]
    }'

The fields in the JSON payload are:

Field	Description
host	The domain of your website.
key	The API key you generated in Step 1.
keyLocation	The public URL of your API key file.
urlList	An array of the new or updated webpage URLs.

You can automate this process in your backend so that this API call is made whenever an article is added or updated. If you are using a CMS, you can trigger the IndexNow call using a webhook.

Here’s a general setup for a webhook:

Grab the URL of the new post from the webhook data.
Fetch the secret IndexNow key from environment variables (never hard-code it!).
Build the JSON payload (as shown in the cURL example).
Use fetch to send the POST request to https://api.indexnow.org/indexnow.

If you are using Next.js, you can use API routes to easily create a webhook endpoint for this process.

My Experience After Implementing IndexNow

I set this up for surajon.dev a few months ago. Before, I'd publish a post and immediately go to Google Search Console and Bing Webmaster Tools to manually request indexing. Even then, it was a gamble.

After implementing IndexNow, I've seen my new articles get indexed on Bing within a few hours, sometimes faster. It's incredibly satisfying to see a post go live and appear in search results the same day.

Conclusion

In short, IndexNow is a no-brainer. It's a simple, free protocol that puts you back in control of your content's lifecycle. For surajon.dev, it has meant faster indexing and less wasted crawl budget. Even without official Google adoption yet, it's an essential, easy win for any publisher who takes SEO and timeliness seriously.

I hope this article has helped you learn something new. Thanks for reading.

EasyWrite.dev

We are building a platform that lets you manage

Your blog strategies🚀
Keyword research🔥
Topic generation🧾
AI Automation🤖
SEO and GEO focus articles📈

You can join it from easywrite.dev.