Annotate traces to improve LLM quality with Datadog LLM Observability

Rashel Hoover Will Potts LLM applications rarely crash. They degrade quietly. Once these applications are shipped to production, subtle quality failures become harder to catch with traditional signals. Tone shifts, hallucinated details, off-topic responses, and incomplete reasoning can emerge while latency and token usage look stable.

To help you review and improve LLM quality at scale, Datadog LLM Observability now includes Automations and Annotation Queues. Automations route production traces to datasets or annotation queues based on configurable rules and sampling strategies. Annotation Queues provide a structured environment for systematic human review of curated traces. Domain experts can apply structured labels and qualitative feedback while viewing the full trace context, including spans, metadata, and evaluation results. Together, these features support a quality improvement workflow that includes issue detection, trace routing and review, and model refinement.

In this post, we’ll show how you can use LLM Observability to:

Route production traces to datasets and annotation queues automaticallyReview LLM traces in context and apply consistent labelsUse annotations to fuel a quality improvement loop

Annotate traces to improve LLM quality with Datadog LLM Observability | Datadog

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