Playbook: Launch a RAG feature end to end

Journey Steps

1. 1Step 1 — Design a chunking strategy for retrieval: start with "Profile source documents: length distribution, headings, tables, code blocks"
2. 2Step 2 — Implement hybrid keyword + vector search: start with "Stand up a lexical index (BM25) alongside your vector index on the same chunks"
3. 3Step 3 — Evaluate RAG answer quality automatically: start with "Freeze 30-100 test questions with known-good source passages"
4. 4Step 4 — Budget LLM latency end to end: start with "Trace one real request through every hop and record percentile timings"
5. 5How it fits together: Sequence matters: chunking decisions gate retrieval quality; evals gate launch. Do not skip golden-set creation even under deadline.

Commands

$ npx skills add aniruddhaadak80/skills --skill rag-pipelines-chunking-strategy && npx skills add aniruddhaadak80/skills --skill rag-pipelines-hybrid-search && npx skills add aniruddhaadak80/skills --skill rag-pipelines-eval-rag-quality && npx skills add aniruddhaadak80/skills --skill inference-mlops-latency-budgeting

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