Retrieval-Augmented Generation (RAG)

Welcome to Retrieval-Augmented Generation

This section explores Retrieval-Augmented Generation (RAG), a technique that enhances language models by retrieving relevant information from external knowledge sources before generating responses.

Vector Databases: Understanding embeddings and vector search
Retrieval Strategies: Methods for finding the most relevant information
Context Integration: Techniques for incorporating retrieved context into generation
RAG Architectures: Different approaches to implementing RAG systems

All RAG Posts

Mastering Chunking Techniques in RAG for Optimal Performance

RAG, Chunking

Neil Dave

Apr 5, 2025

Introduction To RAG

Retrieval-Augmented Generation (RAG) is a framework that enhances Large Language Models (LLMs) by combining them with a retrieval system to access external knowledge during…

Ravindra

Apr 3, 2025