Forem: Jenni Juli

What programming language is used in Playwright?

Jenni Juli — Tue, 10 Feb 2026 12:52:17 +0000

Playwright supports multiple programming languages, including JavaScript, TypeScript, Python, Java, and C#. However, JavaScript and TypeScript are the most commonly used because Playwright was created by Microsoft with a strong focus on modern web development.
Java and Python with Playwright at Credo Systemz are also gaining popularity, especially in companies transitioning from Selenium. The flexibility of language support makes Playwright attractive for testers from different backgrounds.

Is Playwright in demand in India?

Jenni Juli — Tue, 10 Feb 2026 12:50:00 +0000

Yes, Playwright is increasingly in demand in India, especially in product-based companies and modern QA teams. Many organizations are moving away from Selenium due to Playwright’s faster execution, auto-wait features, and better handling of modern web apps.
While Selenium still dominates the market, Playwright is considered a future-ready skill. Professionals who know both Selenium and Playwright (Credo Systemz) have a strong advantage and better job stability in the automation testing field.

Is Agentic AI the Future of Automation?

Jenni Juli — Mon, 09 Feb 2026 13:38:55 +0000

Introduction
Automation has evolved from simple scripts to intelligent bots. Agentic AI represents the next generation of automation, where systems don’t just follow rules but they think, decide, and optimize.

Why Traditional Automation Is Limited

*Traditional automation tools:
*

Follow predefined rules
Fail in dynamic environments
Require frequent human intervention
How Agentic AI Transforms Automation

*Agentic AI introduces adaptive automation that:
*

Understands context
Adjusts strategies in real time
Handles exceptions without manual help

Real-World Use Cases

Customer Support: AI agents resolving tickets without escalation
IT Operations: Auto-detecting and fixing system failures
Finance: Autonomous fraud detection and response
Healthcare: AI agents coordinating patient care workflows

Example:

In an e-commerce company:

Agentic AI monitors sales drops
Analyzes customer behavior
Adjusts pricing
Launches targeted campaigns
Reports performance
All without human input.
Challenges
Ethical decision-making
Data security
Over-reliance on automation

Conclusion
Yes, Agentic AI is the future of automation. It shifts automation from “doing tasks” to “achieving outcomes,” making businesses faster, smarter, and more efficient. Credo Systemz Agentic AI training is the best training course to develop the skills of Agentic AI.

What is Agentic AI and how is it different from Generative AI?

Jenni Juli — Sun, 08 Feb 2026 10:36:51 +0000

Agentic AI vs Generative AI
Artificial Intelligence has evolved rapidly from rule-based systems to Generative AI and now to Agentic AI. While Generative AI focuses on creating content, Agentic AI goes a step further by acting autonomously to achieve goals. Understanding the difference is crucial for businesses and professionals planning their AI journey.

**Generative AI
**Generative AI refers to AI systems that generate new content such as text, images, code, music, or videos based on prompts.

*Examples of Generative AI:
*

ChatGPT writing articles or emails
DALL·E generating images
GitHub Copilot suggesting code
Generative AI responds when asked but does not take independent actions.

**Agentic AI
**Agentic AI consists of AI agents that can plan, decide, act, and adapt to achieve a goal with minimal human intervention.

*Key Capabilities of Agentic AI:
*

Goal-oriented behavior
Decision-making and reasoning
Tool usage (APIs, databases, software)
Continuous learning and feedback loops

*Example of Agentic AI:
*
Imagine an AI travel agent:

Understands your budget and preferences
Searches flights and hotels
Compares prices
Books tickets
Sends reminders
This AI doesn’t just generate text but it executes tasks end-to-end.

Agentic AI vs Generative AI

Conclusion
Generative AI is about creation while Agentic AI is about action. Agentic AI builds on Generative AI to create intelligent systems that behave more like digital employees. Learn about Generative AI and Agentic AI at Credo Systemz courses.

Generative AI vs Data Science – which has better scope?

Jenni Juli — Sat, 07 Feb 2026 13:07:30 +0000

🔥** Generative AI vs Data Science — Which Has Better Scope? (India + Global)**

This comes up a LOT lately, so here’s a practical take 👇

🧠 Data Science (DS)

Status: Mature, stable, widely adopted

What you work on

Data cleaning, EDA, dashboards
Classical ML (regression, trees, XGBoost)
Business analytics & experimentation

Pros

Tons of jobs across industries
Clear learning path
Easier entry for freshers

Cons

Market is crowded
Many roles are analytics-heavy, not “real ML”
Salary growth can plateau unless you move to ML/AI roles
Typical scope
Stable long-term career
Incremental growth
Less hype, more predictability

🤖 Generative AI (GenAI)

Status: Exploding, high demand, low supply

*What you work on
*

LLMs, RAG, agents, copilots
Prompting + orchestration (LangChain, LlamaIndex, AutoGen)
AI products, automation, decision-making systems

Pros

Strong demand–supply gap
Faster salary jumps
More product & startup exposure
Global remote opportunities

Cons

Rapidly changing tech
Requires strong CS + ML + systems thinking
Harder to stay relevant without continuous learning
Typical scope
High upside
Faster career acceleration
More risk, more reward

How Do I Start a Career in Data Analytics?

Jenni Juli — Wed, 04 Feb 2026 12:45:11 +0000

Introduction
Companies across industries rely on data analysts to turn raw data into actionable insights. This makes data analytics the fastest-growing and most in-demand career path in 2026. If you’re asking, “How do I start a career in data analytics?”, this step-by-step guide will walk you from absolute beginner to job-ready data analyst.

Why Choose a Career in Data Analytics in 2026?

As data analytics offers high demand across industries, competitive salaries and global opportunities. It ensures clear growth paths into data science, analytics engineering, and AI roles.

Step 1: Understand What a Data Analyst Does

Before you begin, understand the role clearly. A data analyst typically:
 Collects and cleans data
 Analyzes datasets using SQL, Excel, or Python
 Builds dashboards and reports
 Identifies trends and patterns
 Supports business decision-making

Step 2: Learn the Core Data Analytics Skills

To start a career in data analytics, you must build these foundations:
Data Analysis & SQL
 Learn SQL queries (SELECT, JOIN, GROUP BY)
 Work with databases and datasets
 Practice real-world data problems
Excel for Data Analysis
 Formulas, Pivot Tables, VLOOKUP
 Data cleaning and basic visualization
Data Visualization Tools
 Power BI or Tableau
 Dashboard creation and storytelling

Step 3: Learn Basic Statistics & Business Concepts

To interpret data and explain insights clearly, you should understand:
 Mean, median, variance, correlation
 Trends and patterns
 KPIs and business metrics

Step 4: Practice with Real Projects

Projects are essential to get hired. So, upload your projects to GitHub or create a portfolio website. To gain the skills of data analytics, join Credo Systemz Data Analytics Training in Chennai with regular practice, live projects, certification and placement assistance.
Examples:
 Sales performance dashboard
 Customer churn analysis
 Marketing campaign analysis
 Financial trend reports

Step 5: Learn Python

Python helps automate analysis and handle large datasets.
Focus on:
 Pandas, NumPy, Matplotlib
 Basic data cleaning and visualization
 Simple automation scripts
Step 6: Use AI Tools to Learn Faster

AI tools accelerate learning. Smart use of AI can reduce learning time significantly.
 Use AI assistants to explain SQL and Python code
 Generate practice questions
 Debug errors instantly
 Simulate interview questions

Step 7: Get Certified & Build Credibility
While not mandatory, certifications help beginners to boost resume visibility and confidence.
 Google Data Analytics Certificate
 Microsoft Power BI Data Analyst
 Industry-recognized bootcamps

Step 8: Apply for Jobs & Prepare for Interviews

Start applying for:

 Data Analyst
 Junior Data Analyst
 Business Analyst
 Reporting Analyst

Prepare for:

 SQL interview questions
 Case studies
 Dashboard walkthroughs
 Behavioral interviews

Final Thoughts

In 2026, data analytics is no longer limited to tech experts. Any motivated beginner can break into this field with the right roadmap and mindset. To start a career in data analytics, Start with core skills, hands-on practice, and real projects, then gradually build confidence with tools and certifications. Join Credo Systemz data analytics training in Chennai to start your career in data analytics.

Can I Be a Data Analyst in 3 Months?

Jenni Juli — Wed, 04 Feb 2026 10:19:06 +0000

Introduction
Data Analytics is one of the fastest-growing and most beginner-friendly tech careers in 2026.
With companies relying heavily on data for decision-making, many learners ask: Can I become a Data Analyst in 3 months?
The honest answer is: Yes at an entry-level or junior level, it is possible, provided you follow
a focused learning plan and practice consistently. Let’s break it down realistically.

Becoming a Data Analyst in 3 Months

In 3 months, you won’t become an expert but you can become job-ready for junior or fresher data analyst roles.
By the end of 3 months, you can learn to:
 Analyze datasets and answer business questions
 Use Excel, SQL, and Power BI / Tableau
 Understand basic statistics
 Build 2–3 portfolio projects
 Clear entry-level data analyst interviews Skills You Must Learn in 3 Months

Excel (Foundation Skill)
You should know:
 VLOOKUP
 Pivot tables
 Conditional formatting
 Data cleaning
Time needed: 2–3 weeks
SQL (Must-Have Skill)
Learn:
 SELECT, WHERE, GROUP BY
 JOINs
 Subqueries
 Basic performance concepts

Time needed: 3–4 weeks

Data Visualization (Power BI / Tableau)
You should be able to:
 Build dashboards
 Create KPIs
 Use filters & slicers
 Tell stories with data
Time needed: 3–4 weeks
Basic Statistics

Focus on:
 Mean, median, mode
 Probability
 Correlation
 Simple trend analysis

Time needed: 1–2 weeks

3-Month Data Analyst Roadmap

Month 1: Foundations

 Excel (advanced basics)
 SQL fundamentals
 Basic statistics

Goal: Clean and analyze simple datasets
Month 2: Tools & Analysis
 Power BI or Tableau
 SQL joins & queries
 Real-world datasets

Goal: Build your first dashboard

Month 3: Projects & Job Prep
 End-to-end analytics projects
 Resume building
 Interview questions & mock interviews

Goal: Become interview-ready

Who Can Become a Data Analyst in 3 Months?
 Freshers
 Non-IT professionals
 Career switchers
 Business, finance, or operations professionals

You do not need programming knowledge to start.

What Jobs Can You Apply for After 3 Months?

 Junior Data Analyst
 Data Analyst Intern
 Business Analyst (Entry Level)
 Reporting Analyst

Your first role helps you gain experience — growth comes quickly after that.

Common Mistakes to Avoid

 Learning too many tools at once
 Ignoring SQL practice
 Skipping projects
 Not applying for jobs early
 Waiting for “perfect” knowledge
Progress beats perfection.

Final Verdict

Yes, you can become a Data Analyst in 3 months — at a beginner or entry level — if you:
 Follow a structured roadmap
 Practice daily (1–2 hours)
 Build real projects
 Focus on job-oriented skills

Many professionals successfully transition into data analytics training within 90 days with the
right focus and consistency.

Agentic AI: The Next AI Evolution of 2026

Jenni Juli — Tue, 03 Feb 2026 11:40:03 +0000

Introduction

Artificial Intelligence (AI) has been evolved from basic rule-based systems and machine learning to advanced generative models like ChatGPT and DALL·E. With continuous evolution, a new concept is beginning to redefine what AI can do: Agentic AI. This next-generation AI is proactive, goal-driven, and capable of autonomous decision-making.

Agentic AI

Agentic AI is defined as AI systems that can power AI agents to achieve specific outcomesbwith the capability of:

Setting goals,
Making decisions,
Learning from interactions,
and taking actions independently

While traditional AI requires human prompts or operates within narrowly defined tasks, Agentic systems operate with a sense of purpose, adapting to changing environments and complex objectives.

Key Features of Agentic AI

The important features of Agentic AI are:

Autonomy,
Goal-Oriented Behavior,
Learning and Adaptation,
Multi-Step Reasoning,
Tool Use and Environment Interaction.
Autonomy: Agentic AI can initiate and perform various actions without direct human intervention. It evaluates the options and selects the most efficient path toward its goal.
Goal-Oriented Behavior: These systems have the capability to set, pursue, and modify goals based on evolving situations.
Learning and Adaptation: Agentic AI can refine its strategies, learn from success and failure, and improve over time through continuous feedback loops,
Multi-Step Reasoning: Agentic models can plan, break down tasks into sub-tasks, and execute them in sequence.
Tool Use and Environment Interaction: Agentic AI can use different tools, access APIs, and even interact with other digital systems or agents.

The few real-world applications of Agentic AI are:

Virtual Personal Assistants
Software Development Automation
Autonomous Scientific Research
Enterprise Process Automation
Robotics and Smart Systems

Virtual Personal Assistants: Agentic AI can perform various tasks of personal assistants autonomously like:

Software Development Automation:
Without human intervention, Tools like DevOps agents can monitor systems, deploy code, fix bugs, and optimize performance.

Autonomous Scientific Research: AI agents can speed up discoveries in pharmaceuticals, materials, and space research by:

Formulating hypotheses,
Designing experiments,
Collecting data,
Refine conclusions

Enterprise Process Automation: In business, Agentic AI can automate entire workflows which includes procurement and logistics to customer support and financial planning.

Robotics and Smart Systems: Agentic AI empowers robots and smart devices to make real-time decisions in dynamic environments like factories, hospitals, and homes.

Challenges and Considerations

The few challenges and considerations faced in the process of building Agentic AI are:

Ethical Decision-Making: How do we ensure that an autonomous agent’s actions align with human values?
Security: Self-directed AI systems could be exploited if not properly safeguarded.
Transparency and Accountability: It’s crucial to trace how and why an agent made a particular decision.
Control: Striking the balance between autonomy and oversight remains a major concern.

The Future of Agentic AI
In the upcoming years, Agentic AI could become the backbone of intelligent systems in every industry where machines can think, act, and achieve.

Agentic AI marks a paradigm shift in how machines operate, performs complex cognitive tasks and works on goals aligned with human intent.

The groundwork is already being laid for the next AI revolution with platforms like

OpenAI’s AutoGPT,
MetaGPT,
Microsoft’s AutoAgent in development

Conclusion
To sum up, Agentic AI is the next big leap in artificial intelligence that leads to self-directed digital agents with the ability to perform long-term planning, and autonomous action. The focus must be on building powerful, ethical, controllable agentic systems and align with human well-being. As we step into this new frontier, the job opportunities will be created limitlessly. To learn about Artificial intelligence, Gen AI, machine learning, join Credo Systemz Agentic AI training and other training courses.

All the best!

What are the core skills for prompt engineers?

Jenni Juli — Thu, 29 Jan 2026 08:26:12 +0000

🔹 Core Skills Every Prompt Engineer Should Have

Prompt engineering is quickly becoming a specialized skill in AI development. A good prompt engineer bridges the gap between human intent and AI output, optimizing models for accuracy, efficiency, and creativity. Here are the key skills:

1️⃣ Understanding of AI & LLMs

Familiarity with large language models (LLMs) and generative AI behavior
Knowing model strengths, weaknesses, and biases
Awareness of token limits, context windows, and temperature settings

2️⃣ Strong Natural Language Skills

Ability to craft precise, unambiguous prompts
Understanding semantics, syntax, and phrasing to influence AI output
Skill in iterative refinement — testing and adjusting prompts for best results

3️⃣ Programming & Scripting

Comfortable with Python, JavaScript, or API usage for AI integration
Can automate prompt testing, preprocessing, or chaining tasks
Experience with AI frameworks like OpenAI API, LangChain, or Hugging Face is a plus

4️⃣ Analytical & Problem-Solving Skills

Evaluate AI responses critically to identify errors or hallucinations
Ability to debug prompts and optimize for clarity and relevance
Knowledge of metrics to measure model performance

5️⃣ Domain Knowledge

Specializing in a domain (e.g., finance, healthcare, legal, creative writing) helps craft context-aware prompts
Understanding audience needs and regulatory/ethical constraints

6️⃣ Creativity & Experimentation

Inventive thinking to explore multiple approaches for prompts
Iterative testing: small tweaks can dramatically change outputs
Designing multi-step “prompt chains” for complex tasks

7️⃣ Collaboration & Communication

Working with developers, data scientists, and product teams
Translating technical possibilities into business or user-facing solutions
Documenting prompt strategies for reproducibility

💡 Pro Tip: Prompt engineering is part art, part science. Mastering it involves both experimentation and structured testing — knowing how to ask is just as important as what to ask.

What are the 5 principles of prompt engineering?

Jenni Juli — Wed, 28 Jan 2026 12:57:29 +0000

🧠 5 Core Principles of Prompt Engineering (for Developers)

Prompt engineering isn’t magic — it’s structured communication. These 5 principles will level up your AI outputs fast.

1️⃣ Be Clear & Specific

Ambiguous prompts = unpredictable results.

❌ “Explain APIs”
✅ “Explain REST APIs in simple terms with a JavaScript example”

👉 Treat prompts like function signatures.

2️⃣ Give Context

LLMs perform better when they know who, why, and how.

Example:

“You are a senior backend engineer. Explain caching to a junior dev.”

Context sets tone, depth, and accuracy.

3️⃣ Define the Output Format

Tell the model exactly how you want the response.

Examples:

Bullet points
Code snippets
Step-by-step guide
JSON response

This reduces rework and hallucinations.

4️⃣ Use Constraints

Constraints guide precision.

Examples:

“Limit the answer to 150 words”
“Use Python only”
“Avoid theoretical explanations”
Think guardrails, not restrictions.

5️⃣ Iterate & Refine

First prompt is rarely perfect.

Dev mindset:

Test
Adjust
Re-prompt

Small tweaks → massive improvements.

⚡ Pro Tip for Devs

Write prompts like you write code:

Intentional
Structured
Testable

Prompt engineering is just debugging language.

💬 What’s your go-to prompt trick? Share it with the community.

Is prompt engineering a good career?

Jenni Juli — Sat, 24 Jan 2026 10:47:31 +0000

✅ Why Prompt Engineering Can Be a Great Career

High Demand & Growing Field

Large language models (LLMs) like GPT, Claude, and others are everywhere.
Companies need people who can write, optimize, and fine-tune prompts to get real business value.
This is still an emerging role — so early movers can establish themselves as experts.

Strong Salaries

Global average: ~$120K–$136K/year.
US senior roles: $180K–$300K+, sometimes with stock/options.
In India, top remote or global roles can reach ₹50L+ per year
These salaries are high relative to typical software engineering roles.

Accessible Entry (if you know AI basics)

You don’t necessarily need a PhD; strong prompting skills, coding basics, and understanding of LLMs can get you hired.
Portfolios of “prompt engineering projects” can make you stand out.

Cross-Industry Opportunities

LLMs are used in tech, finance, healthcare, marketing, gaming, and more.
Once skilled, you can pivot into AI consulting, ML ops, data science, or AI product roles.

Remote-Friendly & Flexible

Many companies hire prompt engineers remotely.
It’s a skill you can monetize globally, not just locally.

⚠️ Challenges / Considerations

Field is Evolving Rapidly

New LLMs, APIs, and tools appear every few months.
You need to keep learning constantly.

Job Security & Role Clarity

Some companies are still experimenting with “prompt engineer” roles.
In some places, it may merge with AI engineer, ML engineer, or data scientist roles.

Limited Scope if Narrow

If you only specialize in prompt writing and ignore ML fundamentals, you might hit a ceiling.
Combining prompts with automation, AI pipelines, or product development increases career longevity.

Prompt Engineering Training in chennai

Where can I learn about prompt engineering?

Jenni Juli — Thu, 22 Jan 2026 06:49:57 +0000

Prompt engineering is becoming a core skill for developers working with LLMs, AI tools, and automation workflows. Whether you’re building apps, optimizing workflows, or experimenting with AI agents, here are reliable places to learn prompt engineering—from fundamentals to advanced techniques.

🧠 Official & Foundational Resources

Start here to understand how models actually respond to prompts.

OpenAI Documentation

Learn core concepts like system prompts, few-shot prompting, and structured outputs.
Best for: understanding how LLMs interpret instructions.

*Anthropic Prompt Engineering Guide
*
Excellent explanations of clarity, role prompting, and chain-of-thought patterns.

📘 Free Courses & Tutorials

*Hands-on learning without a paywall.
*

DeepLearning.AI – ChatGPT Prompt Engineering for Developers
Short, practical lessons with real examples.
Great for developers transitioning into AI work.

-** Learn Prompting (learnprompting.org)**

One of the most comprehensive free resources available.
Covers basics → advanced techniques → prompt security.

🧪 Interactive & Practice-Based Learning

Learn by experimenting, not just reading.

Prompt Engineering Playgrounds

Use tools like OpenAI Playground to test prompt variations in real time.

Prompt Libraries (GitHub)

Search for:

awesome prompt engineering
llm prompts
chatgpt prompts for developers
Reading real prompts used in production is extremely valuable.

Learn From Here also - Prompt Engineering Training in chennai