Assembling the RAG Pipeline

Pipeline

Part of: Build a RAG System

You have the pieces: chunk, embed, store, search. Now you wire them into one flow and, critically, build the prompt that turns retrieved text into a grounded answer. The end-to-end flow Everything before "send to LLM" you already know how to do. The new skill is the prompt assembly in the middle. This is where most RAG systems quietly fail, so it earns real attention. A good RAG prompt does three jobs 1. Supplies the context. Paste the retrieved chunks in clearly, usually as a labeled block. 2. Constrains the model to it. Tell it, in plain words, to answer using only the provided context. 3. Gives it an out. Tell it to say "I don't know" when the context doesn't contain the answer. Without this, the model falls back to guessing, the exact hallucination you're trying to kill. That "say you don't know" instruction is not optional polish. It's the safety valve. A RAG system that admits ignorance is trustworthy; one that bluffs when retrieval misses is worse than no system at all. Why retrieval and prompting are partners Retrieval decides what the model sees. Prompting decides how it's allowed to use it. Get retrieval right but write a sloppy prompt, and the model wanders off into its

Challenge: Assemble the RAG Pipeline