System Design · advanced
RAG System Design
Architecting a production RAG system: ingestion, retrieval, generation, evals.
Mental model
RAG system design is two pipelines and a contract: an offline ingestion pipeline (chunk → embed → index) and an online query pipeline (retrieve → rerank → pack → generate), bound by an eval harness that gates every change.
How to study RAG System Design
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Building Effective AI Agents (Anthropic Engineering), Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al.), Anthropic — Contextual Retrieval to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare RAG System Design with Chunking, LLM Evals. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Production RAG pipeline and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Common mistakes
- No offline/online separation
- No eval gate, so quality regresses silently
- Ignoring freshness and re-indexing
Learn from primary sources
Practice and explain it back
Production RAG pipeline
Design a RAG system: doc ingestion → chunking → embedding → vector index, plus query-time retrieval → rerank → LLM. Identify the 3 most likely quality regressions.
Expected evidence: Pipeline + the evals you would run on every change.
Open the interactive drill →Review prompts
- What are the two pipelines in a RAG system, and what binds them?
Build evidence
RAG system design doc
An architecture doc for a production RAG system with an eval gate.
- Offline ingestion and online query pipelines defined
- Eval harness as part of the architecture
- Freshness/re-indexing strategy