Forem

# 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.

Posts

👋 Sign in for the ability to sort posts by relevant, latest, or top.
Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)
Cover image for Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)

Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)

3
Comments
3 min read
Building a Local Documentation Chatbot with Python, FAISS, and OpenAI
Cover image for Building a Local Documentation Chatbot with Python, FAISS, and OpenAI

Building a Local Documentation Chatbot with Python, FAISS, and OpenAI

2
Comments
7 min read
AWS User Group Chennai Meetup - Session 3: A Serverless AI-Powered e-Learning Assistant
Cover image for AWS User Group Chennai Meetup - Session 3: A Serverless AI-Powered e-Learning Assistant

AWS User Group Chennai Meetup - Session 3: A Serverless AI-Powered e-Learning Assistant

7
Comments 1
6 min read
đŸŽ„ Model Context Protocol (MCP) Clearly Explained in Hindi

đŸŽ„ Model Context Protocol (MCP) Clearly Explained in Hindi

1
Comments
1 min read
From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence
Cover image for From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence

From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence

20
Comments
11 min read
Quick Framework and some Performance Improvements
Cover image for Quick Framework and some Performance Improvements

Quick Framework and some Performance Improvements

1
Comments 1
6 min read
Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG
Cover image for Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG

Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG

3
Comments 2
8 min read
Spring AI: An Engineer’s Answer to the HR Black Hole
Cover image for Spring AI: An Engineer’s Answer to the HR Black Hole

Spring AI: An Engineer’s Answer to the HR Black Hole

3
Comments
13 min read
Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation
Cover image for Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation

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

Comments
9 min read
From LLMs to Liability: How Agents Grow Up

From LLMs to Liability: How Agents Grow Up

1
Comments
1 min read
Context Engineering: The Missing Piece in Building AI Agents That Actually Work
Cover image for Context Engineering: The Missing Piece in Building AI Agents That Actually Work

Context Engineering: The Missing Piece in Building AI Agents That Actually Work

Comments 1
4 min read
OrKa 0.9.4: cleaner logs, full GraphScout paths, ISO timestamps

OrKa 0.9.4: cleaner logs, full GraphScout paths, ISO timestamps

2
Comments
1 min read
I Created an AI Assistant That Reads the Fine Print for You

I Created an AI Assistant That Reads the Fine Print for You

6
Comments 1
4 min read
Why Most RAG Pipelines Fail in Production (and How to Fix Them)
Cover image for Why Most RAG Pipelines Fail in Production (and How to Fix Them)

Why Most RAG Pipelines Fail in Production (and How to Fix Them)

3
Comments
2 min read
RAG for Dummies
Cover image for RAG for Dummies

RAG for Dummies

5
Comments
2 min read
👋 Sign in for the ability to sort posts by relevant, latest, or top.