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.
Tired of ChatGPT "forgetting" context, so I engineered a Private "Second Brain" using MERN & Local Llama 3 🧠
Cover image for Tired of ChatGPT "forgetting" context, so I engineered a Private "Second Brain" using MERN & Local Llama 3 🧠

Tired of ChatGPT "forgetting" context, so I engineered a Private "Second Brain" using MERN & Local Llama 3 🧠

Comments
3 min read
Demystifying the AI Jungle: Connecting the Dots

Demystifying the AI Jungle: Connecting the Dots

Comments
5 min read
How a RAG Agent Helped My Father's Shoulder Treatment (And Saved ₹30,000).
Cover image for How a RAG Agent Helped My Father's Shoulder Treatment (And Saved ₹30,000).

How a RAG Agent Helped My Father's Shoulder Treatment (And Saved ₹30,000).

4
Comments
5 min read
The RAG System That Retrieved Perfect Chunks (But Answered Wrong Anyway)
Cover image for The RAG System That Retrieved Perfect Chunks (But Answered Wrong Anyway)

The RAG System That Retrieved Perfect Chunks (But Answered Wrong Anyway)

Comments
4 min read
The RAG System That Mixed Documentation From Different Products (And Created Frankenstein Instructions)
Cover image for The RAG System That Mixed Documentation From Different Products (And Created Frankenstein Instructions)

The RAG System That Mixed Documentation From Different Products (And Created Frankenstein Instructions)

Comments
5 min read
Self-RAG vs Adaptive RAG vs Corrective RAG
Cover image for Self-RAG vs Adaptive RAG vs Corrective RAG

Self-RAG vs Adaptive RAG vs Corrective RAG

Comments
3 min read
The RAG System That Found Contradicting Answers (And Confidently Picked The Wrong One)
Cover image for The RAG System That Found Contradicting Answers (And Confidently Picked The Wrong One)

The RAG System That Found Contradicting Answers (And Confidently Picked The Wrong One)

Comments
4 min read
🥽 Deep Dive: Understanding Contextual Recall 🎯 in RAG Systems
Cover image for 🥽 Deep Dive: Understanding Contextual Recall 🎯 in RAG Systems

🥽 Deep Dive: Understanding Contextual Recall 🎯 in RAG Systems

Comments
3 min read
Building an Autonomous RFP Response Engine with Python
Cover image for Building an Autonomous RFP Response Engine with Python

Building an Autonomous RFP Response Engine with Python

Comments
5 min read
The Context Window Paradox: Why Bigger Isn't Always Better in AI
Cover image for The Context Window Paradox: Why Bigger Isn't Always Better in AI

The Context Window Paradox: Why Bigger Isn't Always Better in AI

1
Comments
19 min read
The FAQ Bot That Made Up Answers When It Couldn’t Find Real Ones
Cover image for The FAQ Bot That Made Up Answers When It Couldn’t Find Real Ones

The FAQ Bot That Made Up Answers When It Couldn’t Find Real Ones

Comments
4 min read
LangChain vs LangGraph vs Semantic Kernel vs Google AI ADK vs CrewAI
Cover image for LangChain vs LangGraph vs Semantic Kernel vs Google AI ADK vs CrewAI

LangChain vs LangGraph vs Semantic Kernel vs Google AI ADK vs CrewAI

1
Comments
3 min read
Why Feature Stores Didn't Fix Training–Serving Skew
Cover image for Why Feature Stores Didn't Fix Training–Serving Skew

Why Feature Stores Didn't Fix Training–Serving Skew

1
Comments
4 min read
站內搜尋加上 AI:使用 Google Vertex AI Search(RAG)打造智慧問答型搜尋
Cover image for 站內搜尋加上 AI:使用 Google Vertex AI Search(RAG)打造智慧問答型搜尋

站內搜尋加上 AI:使用 Google Vertex AI Search(RAG)打造智慧問答型搜尋

Comments
4 min read
The Quiet Rebellion: Waking Up Your AI
Cover image for The Quiet Rebellion: Waking Up Your AI

The Quiet Rebellion: Waking Up Your AI

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