Rag Architect

Rag Architect

Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthu

Optimized workflow

This edition turns the source methodology into a repeatable agent workflow with explicit inputs, checkpoints and deliverables.

Quality standard

• Confirm scope and missing inputs before execution
• Ground decisions in available evidence and preserve source constraints
• Return an actionable result with assumptions, risks and next steps

Agent compatibility

The same core method is packaged for Claude, Codex, GPT, Gemini, Cursor and OpenCode.