Use this to measure and monitor the quality of a RAG (retrieval-augmented generation) pipeline - whether it retrieves the right context and answers faithfully. Trigger on "my RAG gives wrong answers", "is my retrieval any good", "the chatbot makes things up", "evaluate my RAG", "improve RAG accuracy". Diagnose whether the failure is in retrieval or generation - they need different fixes.
How to install monitor-rag-quality?
contextjet-ai/awesome-llm-observability/monitor-rag-quality
$npx -y skills add contextjet-ai/awesome-llm-observability --skill monitor-rag-qualityInstalls into the current project.
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Run `npx skills use "https://github.com/contextjet-ai/awesome-llm-observability" --skill "contextjet-ai/awesome-llm-observability/monitor-rag-quality"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/contextjet-ai/awesome-llm-observability" that are relevant to the current task. Run `npx skills add "https://github.com/contextjet-ai/awesome-llm-observability"` and select the relevant skills, then follow their instructions.
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