RAG Engineering Mastery10 / 10
The Production RAG Reference Architecture
Every piece, assembled: ingestion, hybrid retrieval, re-ranking, grounded generation, guardrails, eval and caching — the blueprint you can ship.
Here is the whole system on one page — the blueprint that turns the previous nine articles into something you can deploy.
The ingestion pipeline (offline)
- Clean source docs (strip boilerplate, fix encoding).
- Chunk structurally, 300–600 tokens, ~15% overlap.
- Enrich each chunk with metadata (source, section, date, url).
- Embed with a versioned model.
- Index into Postgres/pgvector with an ANN index + a keyword index.
The query pipeline (online)
- (Optional) Rewrite the query with a small model.
- Hybrid retrieve — vector + keyword, fused with RRF, top 30–50.
- Re-rank with a cross-encoder; keep top 3–8.
- Confidence gate — if the top score is weak, return "I don't know."
- Generate grounded, with citations, from the kept chunks.
- Faithfulness check the output; cache the answer.
The loop that keeps it honest
Wrap it in evaluation + observability: run the eval set on every change (recall, faithfulness, relevance), and log real queries with their retrieval scores so you can grow the eval set from production.
That's production RAG: measurable retrieval, grounded generation, honest under uncertainty, and affordable at scale. You now have the map and the mechanics.
Series — RAG Engineering Mastery
- Part 01Why Naive RAG Fails in ProductionThe 50-line vector-search demo that wows in a notebook falls apart the moment real users ask real questions. Here is why — and the map out.
- Part 02Chunking — The Decision That Sets Your CeilingYou can't retrieve what you chunked badly. Chunking is the most under-rated lever in RAG — and the cheapest to get right.
- Part 03Embeddings & Vector Stores 101An embedding turns meaning into geometry. A vector store makes that geometry searchable in milliseconds. Get both right and retrieval gets easy.
- Part 04Hybrid Retrieval — Keyword + VectorVector search understands meaning but fumbles exact terms, IDs, and rare words. Keyword search nails those and misses paraphrase. Use both.
- Part 05Re-Ranking — The Cheap Quality WinRetrieval gets you 30 plausible chunks. A re-ranker reads them against the actual question and floats the truly relevant few to the top.
- Part 06Prompting the Generator — Grounding & CitationsGreat retrieval is wasted if the model ignores it or can't point to its sources. Grounding is a prompt-design discipline, not an afterthought.
- Part 07Evaluation — You Can't Improve What You Don't MeasureWithout an eval set, every RAG change is a vibe. With one, you tune chunking, retrieval and prompts with a number that tells you if you helped or hurt.
- Part 08Handling Hallucinations & GuardrailsWhen retrieval comes up empty, a helpful model invents. Guardrails turn 'confidently wrong' into 'honestly unsure' — the difference users actually trust.
- Part 09Cost & Latency DisciplineA RAG query touches embeddings, a vector DB, a re-ranker and an LLM. Each adds milliseconds and cents. At scale, discipline here is the difference between a margin and a bonfire.
- Part 10The Production RAG Reference Architecture — you are hereEvery piece, assembled: ingestion, hybrid retrieval, re-ranking, grounded generation, guardrails, eval and caching — the blueprint you can ship.