Retrieval-Augmented Generation (RAG) has become the standard architecture for building LLM applications that need access to external knowledge. This deep dive covers the technical details.
RAG Architecture Overview
A RAG system has three main components:
- Indexing pipeline: Process documents into searchable chunks with embeddings
- Retrieval system: Find relevant chunks for a given query
- Generation system: Feed retrieved context to an LLM for response generation
Chunking Strategies
How you split documents matters enormously:
Fixed-size chunks: Simple but can split sentences and lose context.
Semantic chunking: Split at natural boundaries (paragraphs, sections).
Recursive chunking: Try multiple strategies and evaluate.
Sliding window: Overlapping chunks to maintain context.
Retrieval Optimization
Hybrid search: Combine vector similarity with keyword matching for better results.
Re-ranking: Use a cross-encoder model to re-rank retrieved chunks.
Multi-query: Generate multiple query variations for comprehensive retrieval.
Generation
Prompt template: Structure how retrieved context is presented to the LLM.
Source citation: Always cite which documents the information comes from.
Fallback handling: What happens when no relevant context is found?
Advanced Topics
Agentic RAG: The agent decides when and what to retrieve.
Graph RAG: Use knowledge graphs for structured retrieval.
Multi-modal RAG: Retrieve images, audio, and video along with text.
Practical Implementation
Start simple. Use a basic vector store with fixed chunking, then optimize based on real usage. Our Build with LLMs programme covers RAG implementation in depth.