Building effective AI evaluations has become a crucial skill for AI product managers, as evidenced by the recent deep dive into constructing AI evals frameworks. Start by conducting a manual error analysis on a significant sample of your application traces, which provides the foundational data needed for the process.
For instance, as suggested by experts in the evals course, begin with open coding—review around 100 independent traces to identify visible issues, such as hallucinations or formatting errors. When you reach a point of saturation where no new failure modes are emerging, organize these initial observations into meaningful clusters, known as axial codes. This classification (e. g.
, 'tour scheduling errors' or 'data formatting issues') helps in prioritizing which issues should be tackled first. Moving forward, integrate automation into your evaluation by developing core code-based evaluators that can automatically flag simple error scenarios.
Additionally, meeting the challenge of nuanced failure modes might require leveraging LLM-as-judge evals, where the language model itself compares outputs against predefined human-labeled benchmarks. Such an automated pipeline can be integrated into your continuous integration (CI) system, ensuring that any new product iterations maintain the quality standards set by your evaluation metrics.
The key takeaway for PMs here is to use these data-driven insights not only for immediate troubleshooting but also as a strategic feedback loop for future development cycles.
By systematically tracking how each iteration of your product responds to these evaluations, you can continually optimize reliability and performance, thereby reinforcing your product’s competitive edge in a rapidly evolving market.