Evals for PMs: A practical guide to AI product quality

AI evaluations (evals) serve as a structured method to measure the quality of AI features, addressing the challenge of non-deterministic outputs that cannot be manually tested. For product managers, evals provide coverage by testing a vast array of scenarios, tradeoff visibility to understand how improvements in one area may affect another, and version comparison to confirm enhancements over previous iterations. The process involves three main components: datasets that represent real user interactions, tasks that are evaluated, and scorers that measure quality across specific dimensions. These evaluations are crucial for making informed product decisions by providing data-backed insights rather than relying solely on engineering assessments. Evals require collaboration among product managers, AI engineers, subject matter experts, and data analysts, and they can be integrated into a continuous improvement loop to enhance AI systems effectively. Tools like Braintrust support this process by allowing various roles to collaborate seamlessly and leverage AI assistants like Loop to accelerate workflows, identify patterns, and optimize tasks without coding, ultimately creating a sustainable evaluation workflow.

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