[go: up one dir, main page]

DEV Community

# 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.
The Context Graph Manifesto

The Context Graph Manifesto

5
Comments
13 min read
Stop Fine-Tuning Everything: Inject Knowledge with Few‑Shot In‑Context Learning

Stop Fine-Tuning Everything: Inject Knowledge with Few‑Shot In‑Context Learning

Comments
16 min read
I Built a Personalized AI Tutor Using RAG – Here's How It Actually Works (And the Code)

I Built a Personalized AI Tutor Using RAG – Here's How It Actually Works (And the Code)

Comments
3 min read
I Built a RAG-Powered “Second Brain” and Accidentally Created My Personal Research Assistant

I Built a RAG-Powered “Second Brain” and Accidentally Created My Personal Research Assistant

Comments
13 min read
RAG Doesn’t Make LLMs Smarter, This Architecture Does

RAG Doesn’t Make LLMs Smarter, This Architecture Does

Comments
4 min read
Como Criar um Chatbot com RAG do Zero: Guia Prático com OpenAI e Qdrant

Como Criar um Chatbot com RAG do Zero: Guia Prático com OpenAI e Qdrant

1
Comments
7 min read
How to Build a Text-to-SQL Agent With RAG, LLMs, and SQL Guards

How to Build a Text-to-SQL Agent With RAG, LLMs, and SQL Guards

Comments
7 min read
Converting Text Documents into Enterprise Ready Knowledge Graphs

Converting Text Documents into Enterprise Ready Knowledge Graphs

Comments
5 min read
Key Benefits of RAG as a Service for Enterprise AI Applications

Key Benefits of RAG as a Service for Enterprise AI Applications

Comments
6 min read
🕸️ Stop Building "Dumb" RAG: Why Vectors Are Not Enough (The GraphRAG Shift)

🕸️ Stop Building "Dumb" RAG: Why Vectors Are Not Enough (The GraphRAG Shift)

Comments
3 min read
Stop Tuning Embeddings: Package Your Knowledge for Retrieval

Stop Tuning Embeddings: Package Your Knowledge for Retrieval

Comments
4 min read
Vectors vs. Keywords: Why "Close Enough" is Dangerous in MedTech RAG

Vectors vs. Keywords: Why "Close Enough" is Dangerous in MedTech RAG

Comments
3 min read
Dense vs Sparse Vector Stores: Which One Should You Use — and When?

Dense vs Sparse Vector Stores: Which One Should You Use — and When?

Comments
2 min read
The Future of Hyper-Local AI

The Future of Hyper-Local AI

Comments 1
1 min read
Building Vroom AI: A Multi-Agent Architecture for Intelligent Driving Education

Building Vroom AI: A Multi-Agent Architecture for Intelligent Driving Education

3
Comments
7 min read
10 Best Practices to Manage Unstructured Data for Enterprises

10 Best Practices to Manage Unstructured Data for Enterprises

Comments
8 min read
Building a Local-First RAG Engine for AI Coding Assistants

Building a Local-First RAG Engine for AI Coding Assistants

Comments
4 min read
Self-Hosting Cognee: LLM Performance Tests

Self-Hosting Cognee: LLM Performance Tests

Comments
9 min read
Clone Your CTO: The Architecture of an 'AI Twin' (DSPy + Unsloth)

Clone Your CTO: The Architecture of an 'AI Twin' (DSPy + Unsloth)

Comments
3 min read
How I Improved RAG Accuracy from 73% to 100% - A Chunking Strategy Comparison

How I Improved RAG Accuracy from 73% to 100% - A Chunking Strategy Comparison

Comments
7 min read
Enterprise-Grade RAG Platform: Orchestrating Amazon Bedrock Agents via Red Hat OpenShift AI

Enterprise-Grade RAG Platform: Orchestrating Amazon Bedrock Agents via Red Hat OpenShift AI

Comments
22 min read
One Year of Model Context Protocol: From Experiment to Industry Standard

One Year of Model Context Protocol: From Experiment to Industry Standard

Comments
3 min read
TOON vs JSON en RAG (Java): el Grinch de los formatos cuando cada token cuenta 🎁

TOON vs JSON en RAG (Java): el Grinch de los formatos cuando cada token cuenta 🎁

Comments
7 min read
Modern Search Techniques for Vector Databases (w/LangChain)

Modern Search Techniques for Vector Databases (w/LangChain)

1
Comments
4 min read
The Research: MiniMax M2.1 (The "Linear" Revolution)

The Research: MiniMax M2.1 (The "Linear" Revolution)

Comments
3 min read
loading...