AI Application Observability: Logs, Traces, Metrics and Evaluation
What to capture when debugging AI applications and how observability connects technical failures to answer quality.
Why AI needs more than application logs
Traditional logs can tell you that an endpoint returned 200. AI applications need to explain why the answer was produced: which model ran, what evidence was retrieved, which tools were called, and whether output validation passed.
Trace the complete request
Assign a request ID and connect API, retrieval, reranking, tool calls, model calls, and validation steps. Record timings for each component so latency regressions are attributable.
Useful metrics
Track request success, answer validation failures, retrieval quality on evaluation traffic, token usage, model latency, tool error rate, and user feedback. Separate technical errors from quality failures.
Privacy-aware logging
Do not automatically log full prompts or sensitive documents. Redact personal or confidential information and define retention rules. Store identifiers that let authorized engineers reproduce a problem safely.
Evaluation in the loop
Connect production traces to evaluation datasets. Sample representative traces for review and convert recurring failure modes into regression tests.
Keep learning
Apply the concepts in a small project, measure the result, document the trade-offs, and explore related TechStudio resources.
Explore Resources