Langfuse

Instrument a development or staging version of the AI application with the Langfuse SDK or supported integration and send traces for a small set of workflows. Add useful metadata such as environment, user-safe identifiers, model, and feature name so failures can be segmented later. Define evaluations for the qualities that matter to the product instead of collecting traces without a review process. Before production rollout, decide which prompt and response content may be stored and redact sensitive fields. Use traces to reproduce failures, then connect fixes to measurable evaluation improvements.

For a first evaluation, keep the scope small enough that you can compare the AI-assisted result with a known baseline. Record the configuration, model or workflow choices that produced the result so successful tests can be reproduced. If the product connects to external systems or private data, grant the minimum permissions required and review the provider's current security and data-handling documentation before expanding access.