Building a Resilient RAG Pipeline
Why most RAG demos break in production, and the retrieval, evaluation, and guardrail patterns that make retrieval-augmented generation dependable at scale.
Retrieval-augmented generation looks deceptively simple in a demo: embed some documents, drop them in a vector store, and let the model answer. Then you ship it, and the answers start drifting. This post walks through the failure modes I see most often and the architecture I default to.
The three failure modes
Almost every RAG incident I've debugged comes down to one of these:
- Retrieval miss — the relevant chunk never made it into the context window.
- Context dilution — too many near-duplicate chunks crowd out the signal.
- Unfounded confidence — the model answers fluently without grounding.
Each needs a different defense, and none of them are solved by "more data."
A retrieval layer you can trust
Start with chunking strategy, not embedding model. The model matters, but a poorly chunked knowledge base will defeat even the best embeddings.
Your retrieval precision is bounded by your chunking, not your embeddings.
For most documentation-style corpora, I use semantic chunking with overlap and
keep a small set of parent documents so I can return surrounding context on a
hit.
Evaluation as a first-class citizen
A RAG system without an eval harness is a RAG system you can't safely change. I treat every prompt, model, or retrieval change as a regression risk and run a golden dataset of question/answer pairs before anything reaches production.
Guardrails, not vibes
Finally: never trust the model to know when it doesn't know. A small classifier on retrieved context — "does this actually support the answer?" — catches the worst hallucinations before they reach a user.
query -> retrieve -> rerank -> grade -> (grounded? generate : fallback)
That fallback branch is the difference between a demo and a product.