Skill: codekungfu/rag-pipelines (version 1.0.0)

--- name: rag-pipelines description: Deep RAG workflow—document ingestion, chunking, metadata, retrieval and reranking, grounding and citations, evaluation, and failure modes (hallucination, staleness). Use when building or debugging retrieval-augmented generation systems. --- # RAG Pipelines (Deep Workflow) RAG quality is dominated by **chunking**, **retrieval**, and **evaluation**—not the LLM alone. Treat the system as data engineering plus generation with explicit failure modes. ## When to Offer This Workflow **Trigger conditions:** - Building Q&A over internal docs, support assistants, or copilots - Hallucinations, wrong citations, or stale answers - New content types (PDF, HTML, code repositories) **Initial offer:** Use **six stages**: (1) task & success criteria, (2) ingestion & cleaning, (3) chunking & metadata, (4) retrieval & rerank, (5) generation & grounding, (6) evaluation & monitoring). Confirm embedding model and retrieval stack (vector DB, search engine, hybrid). --- ## Stage 1: Task & Success Criteria **Goal:** Define what a “good” answer contains: required citations, length, tone, and when to refuse. **Exit condition:** Written rubric with examples of acceptable vs unacceptable answers. --- ## Stage 2: Ingestion & Cleaning **Goal:** Deterministic text extraction (strip boilerplate, handle PDF/OCR if needed); deduplicate documents; track source URL and `updated_at` for staleness. ### Practices - Version pipelines when parsers change (re-embed job) --- ## Stage 3: Chunking & Metadata **Goal:** Tune chunk size and overlap to query patterns—not one global token count for all content. ### Practices - Attach metadata for ACL filtering (tenant, product area) - Prefer structure-aware splits for docs (headings, sections) --- ## Stage 4: Retrieval & Rerank **Goal:** Hybrid lexical + dense retrieval often beats vector-only for keyword-heavy queries. ### Practices - Cross-encoder reranking on top-k for quality (watch latency) - Query rewriting for multi-turn contexts --- ## Stage 5: Generation & Grounding **Goal:** System prompts that require using only provided context; explicit “not found” behavior; optional citation format (snippet, doc id, link). --- ## Stage 6: Evaluation & Monitoring **Goal:** Offline golden questions with expected supporting docs; online thumbs-down reasons; monitor retrieval hit rate, nDCG@k, and age of sources used. --- ## Final Review Checklist - [ ] Rubric and refusal behavior defined - [ ] Ingestion deterministic; dedupe and versioning - [ ] Chunking and metadata match queries and ACLs - [ ] Hybrid retrieval and rerank tuned with metrics - [ ] Grounding and citation behavior enforced in prompts - [ ] Offline eval plus production monitoring ## Tips for Effective Guidance - Debug retrieval before blaming the LLM. - Long chunks hurt precision; short chunks hurt context—sweep experiments. - See also **vector-databases** and **llm-evaluation** skills for depth. ## Handling Deviations - **Code RAG:** symbol- or AST-aware chunking often beats line-based splits. - **High-stakes domains:** add human review gates and audit logs for sources cited.


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