rag-systems

name	rag-systems
description	Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and grounding LLM responses in factual data.

RAG Systems

Build Retrieval-Augmented Generation systems that enhance LLMs with external knowledge.

Overview

RAG combines information retrieval with language generation, enabling LLMs to access and incorporate external knowledge for more accurate, up-to-date responses.

Quick Reference

Scenario	Recommended Approach	Reference File
Document ingestion and vectorization	Chunking, embedding, indexing	/references/data-ingestion.md
Finding relevant information	Retrieval strategies and ranking	/references/retrieval-methods.md
Combining retrieval with generation	Prompt augmentation and generation	/references/generation-techniques.md

Core Principles

1. Chunking - Split documents into semantically coherent pieces
2. Embedding - Convert text to dense vector representations
3. Indexing - Store vectors for efficient similarity search
4. Retrieval - Find most relevant chunks for query
5. Augmentation - Inject retrieved context into LLM prompt

RAG Pipeline

1. Data Ingestion

• Load documents from various sources
• Split into chunks (200-500 tokens typical)
• Generate embeddings using embedding model
• Store in vector database