rag-design-doc
Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion & chunking, embeddings & index, retrieval & reranking, the generation prompt, grounding/citations, evaluation, and failure modes with mitigations.
npx skills add mohitagw15856/pm-claude-skills --skill rag-design-doc --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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] **1. Goal & non-goals** — what questions it answers well, and what it explicitly won't do. **2. Ingestion & chunking** - Source connectors and refresh strategy (full re-index vs. incremental). - **Chunking:** strategy (fixed, recursive, semantic, structure-aware), size + overlap, and what metadata travels with each chunk (s
- Required Inputs
- Output Format
- RAG Design: [system]
- Quality Checks
- Anti-Patterns
- Based On
What does the rag-design-doc skill do?
Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion & chunking, embeddings & index, retrieval & reranking, the generation prompt, grounding/citations, evaluation, and failure modes with mitigations.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill rag-design-doc --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.