What do I need to use RAG Design Doc?

Corpus; Queries; Grounding requirement; Constraints.

Is RAG Design Doc free to use?

Yes — run it free in the browser playground (no API key needed for the sponsored trial, or bring any provider key), or install it into Claude Code, Cursor, ChatGPT and more via npx pm-claude-skills. MIT-licensed and open source.

RAG Design Doc

Q: How do I know the RAG Design Doc output is good?

The skill verifies itself against its own quality checks: Retrieval quality is evaluated **separately** from answer quality (you can't fix what you can't isolate); The system can say "I don't know" when context is insufficient — it's not forced to answer; Answers carry citations that are verified against the retrieved context; Chunking strategy and size are justified against the corpus structure, not copied from a tutorial.

🔵 Stable🕐 updated 2026-06-24 🔷 SkillSpec L3 pm-ai

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.

▶ Run it free — no key needed 📝 Grade your existing draft View SKILL.md ↗

What to give it

▸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.

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

✓Retrieval quality is evaluated **separately** from answer quality (you can't fix what you can't isolate)

✓The system can say "I don't know" when context is insufficient — it's not forced to answer

✓Answers carry citations that are verified against the retrieved context

✓Chunking strategy and size are justified against the corpus structure, not copied from a tutorial

✓Per-tenant / permission isolation is handled in retrieval, not just at the UI

✓Hybrid (keyword + vector) retrieval is considered for queries with exact terms/IDs

⚠️ What it refuses to do

Do not jump to "fine-tune the model" when retrieval is the problem — fix what's retrieved first

Do not evaluate only the final answer — a good answer from luck and a bad answer from bad retrieval look different and need different fixes

Do not force an answer when nothing relevant was retrieved — an honest "I don't know" beats a confident hallucination

Do not ignore metadata filtering — semantic similarity will happily return the right-sounding chunk from the wrong document or wrong tenant

Do not pick a chunk size by default — it's the single biggest lever on retrieval quality

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcp

Related skills

🔌 Embed this skill

Drop this on your blog, docs, or site — it renders a "Run this skill" card:

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💬 Discussion

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