RAG Design Doc Skill

Most RAG systems fail not at generation but at retrieval — the model answers confidently from the wrong chunks. This skill forces the decisions that actually determine quality (chunking, retrieval, reranking, grounding) and pairs each with how you'll evaluate it, so "it hallucinates sometimes" becomes a diagnosable, fixable pipeline.

Required Inputs

Ask for these only if they aren't already provided:

• Corpus — what's being retrieved over (docs, tickets, code, tables), size, and update frequency.
• Queries — the kinds of questions users ask, and how precise/recall-sensitive they are.
• Grounding requirement — must answers cite sources? Is "I don't know" acceptable (it should be)?
• Constraints — latency budget, cost, privacy/tenancy (per-customer isolation?), and freshness needs.

Output Format

RAG Design: [system]