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# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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RAG-Powered Chat: OpenAI & ChromaDB Integration
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RAG-Powered Chat: OpenAI & ChromaDB Integration

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5 min read
How I Built an AI Workspace To Help Students & Researchers
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How I Built an AI Workspace To Help Students & Researchers

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2 min read
What is Context Engineering?
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What is Context Engineering?

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12 min read
Spring AI: How to use Generative AI and apply RAG?

Spring AI: How to use Generative AI and apply RAG?

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10 min read
We built memory for AI apps focusing on individuals and achieved SOTA (88.24%) on LoCoMo benchmark
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We built memory for AI apps focusing on individuals and achieved SOTA (88.24%) on LoCoMo benchmark

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2 min read
The Future of Document Scanning: A Look at LLM-Powered OCR

The Future of Document Scanning: A Look at LLM-Powered OCR

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12 min read
GenAI Foundations – Chapter 5: Project Planning with the Generative AI Canvas
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GenAI Foundations – Chapter 5: Project Planning with the Generative AI Canvas

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4 min read
GenAI Foundations – Chapter 4: Model Customization & Evaluation – Can We Trust the Outputs?
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GenAI Foundations – Chapter 4: Model Customization & Evaluation – Can We Trust the Outputs?

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9 min read
GenAI Foundations – Chapter 1: Prompt Basics: From Theory to Practice
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GenAI Foundations – Chapter 1: Prompt Basics: From Theory to Practice

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7 min read
Building a 'Chat with Your Logs' System on AWS Using OpenSearch Serverless and Bedrock

Building a 'Chat with Your Logs' System on AWS Using OpenSearch Serverless and Bedrock

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7 min read
RAG Firewall: The missing retrieval-time security layer for LLMs (v0.4.1)
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RAG Firewall: The missing retrieval-time security layer for LLMs (v0.4.1)

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2 min read
Why Agents, Not Just LLMs?
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Why Agents, Not Just LLMs?

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2 min read
Building a Local Documentation Chatbot with Python, FAISS, and OpenAI
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Building a Local Documentation Chatbot with Python, FAISS, and OpenAI

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7 min read
Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation
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Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation

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9 min read
**Processing Mode**

**Processing Mode**

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3 min read
💡 What's new in txtai 9.0

💡 What's new in txtai 9.0

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5 min read
Day 7 — FAISS empty vectors, metric mismatch, and recall collapse (ProblemMap No.8)

Day 7 — FAISS empty vectors, metric mismatch, and recall collapse (ProblemMap No.8)

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3 min read
How We Used RAG to Power an AI-First Internal Tool Builder
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How We Used RAG to Power an AI-First Internal Tool Builder

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2 min read
AI Made Simple: Understanding LLMs, RAG, and MCP Servers 🤖

AI Made Simple: Understanding LLMs, RAG, and MCP Servers 🤖

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2 min read
is RAG dead? nope—it learned to drive

is RAG dead? nope—it learned to drive

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1 min read
🤖 AI Web Scraper & Q&A
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🤖 AI Web Scraper & Q&A

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4 min read
Semantic Embedding in RAG: why close vectors still miss meaning and how to fix it

Semantic Embedding in RAG: why close vectors still miss meaning and how to fix it

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4 min read
How To Use LLMs: Retrieval-Augmented Generation (RAG Systems)

How To Use LLMs: Retrieval-Augmented Generation (RAG Systems)

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5 min read
Let's Build a Voice RAG System That Actually Works 🎉

Let's Build a Voice RAG System That Actually Works 🎉

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20 min read
Driving AI Visibility in Search with Smart LLM Optimization
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Driving AI Visibility in Search with Smart LLM Optimization

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9 min read
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