Building an LLM evaluation framework: best practices

This Datadog article highlights the importance of tracing LLM requests to understand performance bottlenecks and identify issues impacting quality. By annotating these traces with relevant metadata (like prompt, model version, and response), teams can pinpoint the cause of poor LLM outputs – whether it's a problematic prompt, slow model, or data issue. This improved observability allows for faster debugging, better model optimization, and ultimately, higher quality LLM applications.