How RAG Pipelines Work: From User Query to AI-Generated Answer

Retrieval-Augmented Generation (RAG) is a technique that allows large language models to access private or up-to-date data not present in their training sets. A production RAG pipeline involves two main workflows: preparing documents through cleaning, chunking, and embedding, and then retrieving relevant chunks at query time to inform the model's response. Chunking strategies involve a trade-off between too much and too little context, and there is no universal optimal chunk size. Retrieval can be done via keyword search, vector-based semantic search, or a hybrid of both, with hybrid approaches often performing best for technical queries containing exact identifiers. Critically, retrieval quality must be evaluated separately from generation quality, since a model can produce confident but incorrect answers when fed poor context.