AI Evaluation and Observability

Understand what happened

AI applications need more than a final response to explain their behavior. I worked on shared tracing and evaluation infrastructure so developers could follow a request through model calls and tools.

My responsibilities included service operation, application integration, and making the resulting information useful during debugging.

Connect debugging with evaluation

Traces help explain an individual failure. Evaluation checks help determine whether that failure belongs to a wider pattern. I worked on both so teams could move from investigating a request to checking a change before release.

The engineering challenge was to make observability part of normal development without requiring every team to become an expert in the underlying infrastructure.