What is RAG?
Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge retrieval. Instead of relying solely on a model's training data, RAG systems fetch relevant documents at query time and use them as context for generating accurate, grounded responses.
The RAG Pipeline
A production RAG system consists of several key stages:
1. Document Ingestion
Raw documents (PDFs, web pages, databases) are processed, cleaned, and prepared for chunking. This stage handles format conversion, metadata extraction, and quality filtering.
2. Chunking Strategy
Breaking documents into appropriately-sized chunks is critical. Common strategies include:
- Fixed-size chunking — Simple but may split semantic units
- Semantic chunking — Uses NLP to respect paragraph and section boundaries
- Recursive chunking — Hierarchically splits documents at natural breakpoints
3. Embedding & Indexing
Chunks are converted to vector embeddings using models like OpenAI's text-embedding-3 or open-source alternatives like BGE. These vectors are stored in a vector database for efficient similarity search.
4. Retrieval & Generation
At query time, the user's question is embedded and matched against the vector index. The top-k most relevant chunks are retrieved and provided as context to the LLM for response generation.
Evaluation Framework
Measuring RAG quality requires evaluating multiple dimensions:
- Retrieval relevance — Are the right documents being retrieved?
- Faithfulness — Does the generated answer stay grounded in the retrieved context?
- Answer completeness — Does the response fully address the query?
Best Practices
- Always include metadata filtering alongside vector search
- Implement hybrid search (vector + keyword) for better recall
- Use re-ranking models to improve retrieval precision
- Monitor and log all pipeline stages for debugging
- Implement human feedback loops for continuous improvement