Retrieval-Augmented Generation: A Technical Deep Dive

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:

1. Indexing pipeline: Process documents into searchable chunks with embeddings
2. Retrieval system: Find relevant chunks for a given query
3. 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.