What do I need to use RAG Architecture Review?

The current architecture; The symptoms; The corpus; Constraints.

Is RAG Architecture Review 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 Architecture Review

Q: How do I know the RAG Architecture Review output is good?

The skill verifies itself against its own quality checks: Every reported symptom is traced to a specific stage, not blamed on "the model"; Retrieval quality and answer quality are evaluated separately (or that gap is finding #1); Findings are severity-ranked and the fix plan is ordered by impact, not by stage order; Hybrid retrieval and reranking are assessed for queries with exact terms/IDs.

🔵 Stable🕐 updated 2026-10-06 ✅ 4.8/5 🔷 SkillSpec L3 pm-ai

Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a 'chat with your docs' feature, or improve an already-built knowledge assistant. Produces a staged review — ingestion, chunking, retrieval, reranking, generation, evaluation — with prioritised findings, root causes, and concrete fixes.

▶ Run it free — no key needed 📝 Grade your existing draft 📱 Use as an app View SKILL.md ↗ What changed

🗣 Say this to your agent

“Audit a RAG pipeline.”“Diagnose wrong/ungrounded answers from a 'chat with your docs' feature.”“Improve an already-built knowledge assistant.”

What to give it

▸The current architecture — ingestion, chunking, embedding model, vector store, retrieval (top-k, hybrid?), reranking, and the generation prompt.

▸The symptoms — examples of bad answers (wrong, ungrounded, stale, refuses) with the expected answer.

▸The corpus — what's retrieved over, its size, structure, and update frequency.

▸Constraints — latency, cost, and per-tenant/permission isolation 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.

✓Every reported symptom is traced to a specific stage, not blamed on "the model"

✓Retrieval quality and answer quality are evaluated separately (or that gap is finding #1)

✓Findings are severity-ranked and the fix plan is ordered by impact, not by stage order

✓Hybrid retrieval and reranking are assessed for queries with exact terms/IDs

✓Grounding instruction and "I don't know" behaviour are checked in the generation stage

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

⚠️ What it refuses to do

Do not recommend fine-tuning the model when the failure is in retrieval — fix what's retrieved first

Do not review only the generation prompt — most RAG quality is won or lost before the LLM sees anything

Do not present findings without severity and priority — a flat list doesn't tell the team what to do Monday

Do not assume the corpus is fine — stale or badly-structured source data caps every downstream stage

Do not skip the eval gap — without separated metrics, every fix is a guess

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 -p pm-claude-skills pm-claude-skills-mcp

Related skills

🔌 Embed this skill

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

<div data-pm-skill="rag-architecture-review"></div>
<script src="https://mohitagw15856.github.io/pm-claude-skills/embed.js" async></script>

💬 Discussion

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