RAG Observability and Evaluations

Run retrieval-augmented generation like a measurable production system, not a black box.

When to Use This Skill

• Deploying a RAG system to production and need quality monitoring
• Setting up automated evaluation pipelines for retrieval and generation
• Debugging hallucination or relevance regressions
• Building dashboards for RAG-specific golden signals
• Establishing quality gates for RAG pipeline changes

Prerequisites

• RAG pipeline with instrumented retrieval and generation stages
• Python 3.10+ with evaluation libraries (ragas, langchain, openai)
• Prometheus endpoint for custom metrics export
• Benchmark dataset with gold-standard question/answer/source triples
• OpenTelemetry SDK integrated into the RAG service