Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI applications that need to work with custom data. After implementing several RAG systems in my projects, I want to share what I've learned about building effective retrieval pipelines.
What is RAG?
RAG combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems fetch relevant documents from a knowledge base and use them to generate more accurate, grounded responses.
The Architecture
A typical RAG pipeline consists of:
- Document Ingestion: Loading and preprocessing documents (PDFs, web pages, databases)
- Chunking: Breaking documents into semantically meaningful segments
- Embedding: Converting chunks into vector representations
- Vector Storage: Storing embeddings in a vector database (Pinecone, Weaviate, Chroma)
- Retrieval: Finding relevant chunks based on query similarity
- Generation: Using retrieved context to generate responses
Key Lessons Learned
- Chunk size matters: Too small and you lose context; too large and you retrieve irrelevant information. I've found 512-1024 tokens with 20% overlap works well for most use cases.
- Hybrid search wins: Combining semantic (vector) search with keyword (BM25) search often outperforms either alone.
- Reranking is crucial: Using a cross-encoder to rerank initial retrieval results significantly improves relevance.
- Evaluation is hard: Building proper evaluation pipelines with human-labeled datasets is essential but often overlooked.
Looking Ahead
RAG continues to evolve rapidly. Techniques like query expansion, multi-step retrieval, and agent-based architectures are pushing the boundaries of what's possible. I'll be exploring these in future posts.