Debugging AI Systems: Observability for Non-Deterministic Behavior

For every request, log: model version, full prompt, sampling parameters, outputs, latency, and user feedback. This baseline enables replay debugging and offline evaluation.

When users report 'the AI is broken,' you need data to reproduce, not guesses.

Use structured logging with consistent fields: request_id, user_id, timestamp, model_version, prompt_tokens, completion_tokens, temperature, top_p, and full input/output.

Store these traces in a queryable system (not just flat logs). You'll need to search by user, by time range, by model version, and by failure patterns.

Track prompt template versions alongside model versions. When behavior changes unexpectedly, you can diff prompts to see what changed.

Hash your prompts and store the hash with each request. This lets you group requests by effective prompt, even across template iterations.

Maintain a golden dataset: inputs with expected outputs. Run new model versions and prompt changes against this dataset before deploying.

Use automated metrics (exact match, semantic similarity, format validation) plus human review on a sample. Catch regressions before users do.

AI failures are rarely one-offs. Look for patterns: does the model fail on specific topics? specific prompt lengths? specific user cohorts?

Cluster failures by similarity (embedding-based clustering of failed inputs). This reveals systematic weaknesses rather than random noise.

Monitor aggregate metrics: error rate, mean response length, refusal rate, tool call success rate, and user satisfaction scores.

Set thresholds and alerts. If error rate suddenly doubles or mean response length drops by 50%, something changed—investigate immediately.

With full traces, you can replay failed requests: same model, same prompt, same parameters. This deterministic reproduction is critical for fixing bugs.

Build tooling to replay requests at scale. When investigating a pattern of failures, replay 100 similar requests and analyze differences.