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rag-architect

Use when designing a RAG pipeline, choosing chunk size, embedding model or vector database, or measuring retrieval quality (precision@k, recall@k, NDCG). Gives sized recommendations and an evaluation plan against ground truth.

engineering/skills/rag-architect/SKILL.md

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SKILL.mdcore · 4,309 bytes
SKILL.mdcore · 4,309 bytes
SKILL.mdcore · 4,309 bytes
SKILL.mdcore · 4,309 bytes
chunking_optimizer.pyruns · 29,512 bytes
rag_pipeline_designer.pyruns · 28,221 bytes
references/chunking_strategies_comparison.mddoc · 10,181 bytes
references/embedding_model_benchmark.mddoc · 12,729 bytes
references/rag_evaluation_framework.mddoc · 15,137 bytes
retrieval_evaluator.pyruns · 23,603 bytes

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# RAG Architect

Design, tune, and evaluate production RAG pipelines with three deterministic tools. Run the tools against the actual corpus and requirements — do not pick chunk sizes or databases by intuition.

## Hard rules

1. **Never present model names or vendor prices as current facts.** Embedding models and vector-DB pricing rot in months. Recommend a *tier* (see table below), name a current-generation candidate, and tell the user to verify against the provider's live pricing page.
2. **Every design ends with an evaluation run.** A RAG design without `retrieval_evaluator.py` numbers is a hypothesis, not a deliverable.
3. **Chunking is corpus-driven.** Run `chunking_optimizer.py` on the real documents before choosing a strategy.

## Embedding model tiers (pattern, not price list)

| Tier | Current-generation examples (verify before use) | When |
|---|---|---|