Production Traces
What are production traces?
Production traces are records of user inputs and LLM responses that occur in a live production environment—the actual interactions between real users and your AI product. Unlike test data or development traces, production traces represent how your AI product performs in the real world.
How are production traces used to measure AI product quality?
Production traces serve as the foundation for evaluating AI product quality. Teams use them to measure how often specific failure modes or errors are occurring with real users.
The process follows an analyze-measure-improve loop:
- Analyze — Review production traces to identify common failure modes or errors
- Measure — Write evaluations (evals) to quantify how often each failure mode occurs in production traces
- Improve — Make changes to address the most frequent or severe failures
Evals can test production traces using datasets, code assertions, or LLM-as-Judge approaches to get accurate measurements of error rates and quality metrics.
How do production traces work as guardrails?
Production traces can also be used as ongoing guardrails for AI products. By running evals against production traces continuously, teams can monitor whether their AI product continues to perform correctly over time.
This requires deploying eval code alongside the production environment so it can analyze real user interactions as they happen. When evals detect quality degradation in production traces, teams can quickly identify and address issues before they impact too many users.
Learn more:
- How I Designed & Implemented Evals for Product Talk's Interview Coach
- Q&A Session: Building AI Evals for the Interview Coach
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