Ground LLM outputs in your own data by retrieving relevant context at query time and feeding it to the model.
Five passes over the same idea, each from a different angle. Do them in order, or jump to whichever you need.
RAG is the workhorse pattern of production AI engineering. Instead of fine-tuning, you index your knowledge base into a vector store, retrieve the top-k most relevant chunks for every query, and feed them into the model as context. The pipeline has four hard problems — chunking, embedding, retrieval, and reranking — each with its own failure modes and ceiling-breaking techniques like tiered retrieval and semantic caching.
Where this topic shows up outside its home domain:
Retrieval is a graph walk — the vector index is a sparse graph, and k-NN search is traversal under a similarity metric.
The vector store is an abstraction over persistence — the Repository pattern tells you how to keep RAG swap-in-ready between Qdrant, pgvector, and Pinecone.
Semantic caching is a reheat of classic cache design — except the key is a similarity threshold, not an equality check.
