How we learned to evaluate the quality of a RAG system with Claude Code

▤ Summary

This article explains how to evaluate the quality of a RAG system using Claude Code, RAGAS, and custom test data. It walks through measuring context precision, context relevance, and context recall, then shows how to package the evaluation flow through FastAPI and MCP. The piece also describes practical implementation details such as CSV/JSONL handling, benchmark scenarios, and a Python-based evaluation script. It finishes by outlining a reusable RAG evaluation skill and the author’s setup for running assessments on Windows.

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