rag-review

name	rag-review
description	Use when reviewing or auditing a RAG pipeline end-to-end against production best practices. Covers all 8 stages — ingestion/chunking, versioning/updates, embedding/indexing, query routing, retrieval/fusion/reranking, generation/abstention, security, and eval/observability. Triggers on phrases like "review RAG pipeline", "audit RAG", "RAG E2E review", "is this RAG production ready", "check RAG implementation", "RAG pre-merge review", "review this RAG code".
allowed-tools	Read, Glob, Grep

RAG E2E Review Checklist

Iron Law

A RAG pipeline ships only when retrieval, generation, security, AND eval all pass. Three out of four is a production incident.

This skill is the single source of truth for "what does a production-grade RAG pipeline look like across all 8 stages." Reviewers cite reference files for detail — they do not restate content here.

How to use this skill

For human users: Invoke /rag-review for an E2E audit. For deeper review, dispatch @rag-implementation-reviewer or @rag-pipeline-reviewer — both auto-load this skill.

For agents consuming this skill: Walk the 8 stages in order. For every check, cite the file:line of the authoritative reference. Don't manufacture findings — if a stage is clean, say so in one line.

Severity rubric

Severity	Meaning	Verdict impact
🔴 Critical	Security incident, data integrity, prompt injection, hallucination on high-risk path	BLOCK
🟠 High	Production correctness, recall ceiling, missing audit log, missing abstention	NEEDS_REVIEW (fix before merge)
🟡 Medium	Quality + ops drift (no eval gate, no per-axis breakdown, missing cache)	NEEDS_REVIEW (recommend)
🟢 Low	Hygiene (missing comments, magic numbers, undocumented alpha)	APPROVE with note

---

Stage 1 — Ingestion & Chunking

Cross-refs: rag-architect/references/advanced-chunking-guide.md, rag-architect/references/table-chunking-strategy.md

#	Check	Severity if failing	Reference
1.1	Parser matches corpus type — not raw pdf.extract_text() for complex PDFs; Unstructured.io or LlamaParse for table-heavy docs	🟠	advanced-chunking-guide.md §6
1.2	Document-aware chunking on headings/sections — NOT fixed-size sliding window as the only strategy	🟠	advanced-chunking-guide.md §1
1.3	Chunk size calibrated by document type: prose 500–800 tokens, structured/SOP 300–600, tables = whole or row-groups	🟡	advanced-chunking-guide.md §1 + rag-operations-guide.md §6
1.4	10–20% overlap on fixed-size strategies (unless chunking on natural boundaries like paragraphs)	🟡	advanced-chunking-guide.md §1
1.5	Stable chunk IDs: (document_id, document_version, chunk_index) — never auto-increment IDs	🟠	advanced-chunking-guide.md §4
1.6	Required metadata per chunk: document_id, chunk_id, tenant_id, source_uri, section_title, page, chunk_index, embedding_model, chunker_version, parser_version, content_hash, created_at	🟠	advanced-chunking-guide.md §4 (16-field schema)
1.7	Tables NOT split mid-row; column headers repeated in every row-group chunk	🔴	table-chunking-strategy.md §3
1.8	High-stakes tables stored with multiple representations (plain text + Markdown + JSON)	🟡	table-chunking-strategy.md §4
1.9	OCR quality gate applied before ingestion: per-character confidence threshold checked, garbled text rejected	🟡	advanced-chunking-guide.md §6
1.10

---

Stage 2 — Versioning, Updates & Deletion

Cross-refs: rag-architect/references/versioning-and-freshness.md, rag-architect/references/incremental-ingestion-guide.md

#	Check	Severity if failing	Reference
2.1	Two-level hash gate implemented: document hash (fast skip) AND chunk hash (exact diff) — NOT document hash alone	🟠	incremental-ingestion-guide.md §1
2.2	Text normalized before hashing (whitespace, page numbers, timestamps stripped) to prevent false re-embeds from formatting noise	🟠	incremental-ingestion-guide.md §2
2.3	Stable chunk IDs enable clean upsert — changed chunk updates in place, does not create duplicate vector	🔴	incremental-ingestion-guide.md §3
2.4	Soft-delete: removed chunks set is_active=false; retrieval filters WHERE is_active = true	🔴	incremental-ingestion-guide.md §4
2.5	Version fields stored per chunk: embedding_model, chunker_version, parser_version, normalizer_version	🟠	incremental-ingestion-guide.md §5
2.6	Versioning metadata: effective_from, effective_to, superseded_at — default retrieval filter superseded_at IS NULL	🟠	versioning-and-freshness.md §2–3
2.7	New document version supersession runs in a single atomic transaction (marks old superseded + inserts new together)	🔴	versioning-and-freshness.md §5
2.8	Historical query detection implemented — temporal keywords trigger effective_from/effective_to filter instead of current-only filter	🟡	versioning-and-freshness.md §4
2.9	GDPR/RTBF: hard-delete across all versions (vector index + metadata + caches); audit log entry retained forever even after deletion	🔴	versioning-and-freshness.md §8

---

Stage 3 — Embedding & Indexing

Cross-refs: vector-database skill, vector-database/references/embedding-migration-guide.md, vector-database/references/ann-vs-knn.md

#	Check	Severity if failing	Reference
3.1	Embedding model pinned as a constant or env config — NEVER inline string literal at each call site	🔴	vector-database/SKILL.md
3.2	embedding_model version stored in chunk metadata so index-time and query-time models can be verified	🔴	advanced-chunking-guide.md §4
3.3	Query and index use the SAME distance operator (cosine <=> matches vector_cosine_ops index)	🔴	vector-database/references/ann-vs-knn.md
3.4	ANN/HNSW used for production retrieval; KNN/Flat reserved for golden-set eval baseline and scoped high-risk paths	🟠	vector-database/references/ann-vs-knn.md Decision rule
3.5	efSearch / nprobe calibrated against KNN ground truth on golden set — never tuned by feel	🟠	vector-database/references/ann-vs-knn.md Tuning
3.6	Null guard: WHERE embedding IS NOT NULL (or equivalent) in retrieval query	🟠	rag-pipeline-reviewer agent checklist
3.7	Batch embedding — NOT one API call per chunk in a loop	🟡	rag-pipeline-reviewer agent checklist
3.8	Embedding model upgrade plan documented (dual-write or shadow index strategy before cutover)	🟡	vector-database/references/embedding-migration-guide.md

---

Stage 4 — Query Understanding & Routing

Cross-refs: rag-architect/references/query-classification-taxonomy.md, rag-architect/references/query-transformation-guide.md

#	Check	Severity if failing	Reference
4.1	Query classification covers the 9 canonical intent classes (factual_lookup, policy_question, structured_record_lookup, comparison, summarization, multi_hop, troubleshooting, personalized, unsafe)	🟠	query-classification-taxonomy.md §1
4.2	Structured record lookups (exact IDs, counts, current account state) route to SQL/API — NOT RAG	🔴	query-classification-taxonomy.md §4
4.3	unsafe_or_disallowed class refuses before any retrieval takes place; attempt is audit-logged	🔴	query-classification-taxonomy.md §1
4.4	Router type matches corpus complexity: rule-based for v1 (< ~30 rules), classifier for more; LLM router only for agentic flows	🟡	query-classification-taxonomy.md §3
4.5	Conversational follow-up queries rewritten (pronouns resolved, standalone query) before retrieval	🟠	query-transformation-guide.md §1
4.6	HyDE NOT deployed on factual, legal, or contractual query types without A/B measured win on golden set	🟠	query-transformation-guide.md §5
4.7	Sub-index routing considered when a corpus segment exceeds ~100k documents	🟡	query-classification-taxonomy.md §4

---

Stage 5 — Retrieval, Fusion & Reranking

Cross-refs: rag-architect/SKILL.md (Default Stack), rag-architect/references/rag-operations-guide.md

#	Check	Severity if failing	Reference
5.1	Hybrid retrieval: dense + BM25 — or documented measured justification for skipping sparse	🟠	rag-architect/SKILL.md Step 1
5.2	Fusion via RRF (k=60) — NOT ad-hoc weighted score blending without normalization	🟠	rag-architect/SKILL.md Step 1
5.3	Pre-filtered ANN (filters in vector DB call) — NEVER post-filter on top-k results	🔴	rag-security-reviewer/SKILL.md §1
5.4	top_k_retrieve >> top_k_final: retrieve 50–80 candidates, rerank down to 5–10 for generation	🟠	rag-operations-guide.md §6
5.5	Cross-encoder reranker present on production paths	🟠	rag-architect/SKILL.md Step 1
5.6	Environment-aware reranker: local/dev uses lighter model (Gemini Flash); staging/prod uses production-grade (Vertex AI Ranking API)	🟡	rag-operations-guide.md §5
5.7	Query embedding cache keys include tenant_id — NOT query text alone	🔴	rag-operations-guide.md §1
5.8	MMR or diversity filter applied during context packing to prevent near-duplicate chunks in final context	🟡	context-packing-patterns.md §1

---

Stage 6 — Generation Contract & Abstention

Cross-refs: rag-architect/references/context-packing-patterns.md, rag-architect/references/abstention-decision-framework.md, rag-architect/references/rag-response-contract.md

#	Check	Severity if failing	Reference
6.1	Strict grounding prompt: "answer ONLY using the provided context" — no room for LLM to draw on training data	🟠	context-packing-patterns.md §3
6.2	Structured answer contract returned (not free-form prose with embedded citations the system can't parse)	🟠	rag-response-contract.md §1
6.3	Per-claim citations — every factual sentence in the answer carries a [chunk_id] reference	🟠	context-packing-patterns.md §3
6.4	schema_version emitted on every response	🟠	rag-response-contract.md §8
6.5	status field is a value from the closed 7-value enum (answered/partial/abstained/clarification_needed/tool_error/policy_blocked/no_retrieval)	🔴	rag-response-contract.md §3
6.6	no_retrieval status is explicitly policy-governed — NOT used as a silent fallback that bypasses RAG	🔴	rag-response-contract.md §3
6.7	confidence_level is a band (high/medium/low) — NEVER raw float exposed to UI	🟠	rag-response-contract.md §4
6.8	Confidence derived from evidence signals (retrieval score, rerank score, citation coverage, freshness), NOT from LLM self-report	🟠	rag-response-contract.md §4
6.9	Faithfulness gate implemented: if faithfulness < threshold (~0.7), band clamped to low regardless of weighted score	🟠	rag-response-contract.md §4
6.10	safety block emitted on every response — missing block ≠ empty

---

Stage 7 — Security

This stage delegates to rag-security-reviewer/SKILL.md — do NOT duplicate it here. Load that skill in parallel when reviewing security-sensitive RAG code. The five items below are the minimum gate; the dedicated skill has full coverage.

#	Minimum gate (full coverage in rag-security-reviewer)	Severity	Reference
7.1	Pre-filtered ANN at a SINGLE chokepoint — NEVER post-filter on top-k for security enforcement	🔴	rag-security-reviewer/SKILL.md §1–2
7.2	Retrieved content treated as untrusted: clear delimiters in prompt, no tool-call execution allowed from retrieved text	🔴	rag-security-reviewer/SKILL.md §3
7.3	Per-response audit log: request_id, user_id, tenant_id, filters_applied, chunks_used, model, prompt_version, latency_ms	🔴	rag-security-reviewer/SKILL.md §4
7.4	PII redaction at ingest AND at response (safety.redactions[])	🔴	rag-security-reviewer/SKILL.md §5 + rag-response-contract.md §6
7.5	Right-to-be-forgotten tested end-to-end: vector index + BM25 index + metadata + all caches; audit log entry retained	🔴	rag-security-reviewer/SKILL.md §6 + versioning-and-freshness.md §8

If ANY 🔴 item in Stage 7 fails, the review verdict is BLOCK regardless of other stages.

---

Stage 8 — Evaluation & Observability

Cross-refs: rag-evaluator/SKILL.md, rag-architect/references/rag-operations-guide.md

#	Check	Severity if failing	Reference
8.1	Golden set exists with ≥ 50 labeled queries — or has a concrete plan with date	🔴	rag-evaluator/SKILL.md Golden set design
8.2	Coverage targets met: ~60% happy path, ~20% edge cases, ~20% known unanswerable	🟠	rag-evaluator/SKILL.md Coverage targets
8.3	CI gate configured: fails build on Recall@K, Faithfulness, or Citation Quality regression beyond tolerance	🔴	rag-evaluator/SKILL.md CI gate pattern
8.4	Abstention metrics tracked as two separate rates — False Abstention Rate + False Answer Rate — NOT collapsed into one metric	🟠	rag-evaluator/SKILL.md Answer metrics
8.5	Eval breakdown by dimension when corpus grows: doc type, recency, query style, query length, corpus segment	🟡	rag-evaluator/SKILL.md Eval Breakdown by Dimension
8.6	LLM-as-judge calibrated against human labels (≥ 80% agreement) before use at scale	🟠	rag-evaluator/SKILL.md LLM-as-judge dangers
8.7	All 5 production dashboards wired: Health (p95 latency), Quality (faithfulness, abstention rate), Cost (per-query cost, cache hit rate), Ingestion (queue depth, failed ingestions), Security (cross-tenant attempts, audit log gaps)	🟠	rag-operations-guide.md §3
8.8	Async eval pattern: sample 1–10% of production traffic via trace store — NOT inline in sync response path	🟡	rag-response-contract.md §1
8.9	Corpus hygiene checks run after bulk ingestion: near-duplicate detection (content_hash dedup), stale/superseded doc sweep, authority tagging for canonical versions	🟡	rag-operations-guide.md §9
8.10	Post-ingestion smoke test runs after every ingestion that changed ≥ 1 chunk; ingestion trace logged	🟡	incremental-ingestion-guide.md §7
8.11	p95 latency target defined and monitored per stage: query embedding ≤ 50ms, ANN retrieval ≤ 200ms, reranker ≤ 250ms, LLM first token ≤ 500ms, total ≤ 2s

---

Verdict format

Output verdict as the final line of the review report:

VERDICT: [APPROVE | NEEDS_REVIEW | BLOCK] — CRITICAL: N | HIGH: N | MEDIUM: N | LOW: N

Rules:

• BLOCK — any 🔴 Critical finding. Must fix before merge.
• NEEDS_REVIEW — zero Critical, but ≥ 1 High or pattern of Medium. Recommend fix.
• APPROVE — zero Critical, zero High, ≤ 3 Medium. Ship with notes on Lows.

---

When NOT to use this skill

Situation	Use instead
Pure security review only	rag-security-reviewer/SKILL.md directly (more depth)
Designing a NEW RAG pipeline	rag-architect/SKILL.md
Diagnosing one failed production query	rag-debugger/SKILL.md 9-layer chain via /rag-debug
Running the eval suite to detect regressions	Dispatch @rag-eval-runner agent
Debugging why corpus-scale accuracy dropped	rag-evaluator/SKILL.md Eval Breakdown by Dimension

---

References table

Stage	Authoritative references
1 — Ingestion & chunking	rag-architect/references/advanced-chunking-guide.md, rag-architect/references/table-chunking-strategy.md
2 — Versioning / updates / deletion	rag-architect/references/versioning-and-freshness.md, rag-architect/references/incremental-ingestion-guide.md
3 — Embedding & indexing	vector-database/SKILL.md, vector-database/references/ann-vs-knn.md, vector-database/references/embedding-migration-guide.md
4 — Query understanding & routing	rag-architect/references/query-classification-taxonomy.md, rag-architect/references/query-transformation-guide.md
5 — Retrieval, fusion & reranking	rag-architect/SKILL.md (Default Stack), rag-architect/references/rag-operations-guide.md
6 — Generation contract & abstention	rag-architect/references/context-packing-patterns.md, rag-architect/references/abstention-decision-framework.md, rag-architect/references/rag-response-contract.md
7 — Security	rag-security-reviewer/SKILL.md (dedicated)
8 — Eval & observability	rag-evaluator/SKILL.md, rag-architect/references/rag-operations-guide.md

---

Maintenance note

When a new reference file is added to any sibling skill (rag-architect, rag-evaluator, rag-security-reviewer, vector-database, rag-debugger), update the corresponding stage table here in the same PR. The skill is the single point of update; the consuming agents (rag-implementation-reviewer, rag-pipeline-reviewer) and command (/rag-review) inherit automatically via skills: frontmatter.