A practical way to reason about chunking, embeddings, ranking, citations, latency, and evaluation in retrieval-augmented generation.
Start with retrieval quality
RAG quality depends on what reaches the generator. If relevant passages are not retrieved, prompt engineering cannot recover them reliably. Chunking, embedding choice, query formulation, filters, and reranking deserve first-class evaluation.
Separate retrieval from generation
A clean RAG architecture treats retrieval as its own subsystem with measurable inputs and outputs. That separation makes it possible to benchmark retrieval recall independently from answer quality.
Use answerable evidence
The best context is not simply the most semantically similar text. It is text that contains enough information to support the answer. Metadata, document structure, and reranking can improve that distinction.
Evaluate citations and abstention
A strong RAG system should know when evidence is insufficient. Citation correctness, groundedness, and refusal or abstention behavior matter alongside traditional retrieval metrics.
Design for change
Embedding models, chunking rules, source documents, and ranking strategies evolve. Versioning the retrieval pipeline makes regressions traceable and allows controlled migration.
- State the problem before the tools.
- Expose the system boundary.
- Use metrics with context and limitations.
- Document one meaningful trade-off.
- Link to adjacent project or topic pages.
What is RAG system design?
A practical way to reason about chunking, embeddings, ranking, citations, latency, and evaluation in retrieval-augmented generation.
Why does it matter?
RAG quality depends on what reaches the generator. If relevant passages are not retrieved, prompt engineering cannot recover them reliably. Chunking, embedding choice, query formulation, filters, and reranking deserve first-class evaluation.