LLM-powered RAG from scratch: a reference architecture for production

What RAG actually solves

RAG is a knowledge boundary, not a model upgrade. It lets a generic LLM answer specific questions about your data without fine-tuning.

The five components

1. Loader. Pulls source documents from your systems of record.
2. Chunker. Splits content into retrievable units. Chunk size dominates quality.
3. Vector store. Indexes embeddings for fast similarity search.
4. Retriever. Picks the top-k results plus rerank.
5. Generator. Composes the answer with citations.

Boring things that matter

Eval pipelines, hallucination tests, cost dashboards and the ability to roll back a chunking change. None of these are exciting, all of them are required.