Don't treat evals as a mere checklist. Instead, use them as a creative tool to discover opportunities. A well-designed eval can reveal that a product is underperforming for a specific user segment, pointing directly to areas for high-impact improvement that a simple "vibe check" would miss.

While evals involve testing, their purpose isn't just to report bugs (information), like traditional QA. For an AI PM, evals are a core tool to actively shape and improve the product's behavior and performance (transformation) by iteratively refining prompts, models, and orchestration layers.

Before building an AI agent, product managers must first create an evaluation set and scorecard. This 'eval-driven development' approach is critical for measuring whether training is improving the model and aligning its progress with the product vision. Without it, you cannot objectively demonstrate progress.

Building non-deterministic AI products fundamentally changes the PM role. Instead of creating detailed, rigid specifications, the PM's primary task becomes defining and codifying "what good looks like." This is done by repeatedly grading AI outputs to train evaluation systems and guide the model's behavior.

Evals transform product specs from ambiguous documents into testable, measurable criteria. This gives product managers more leverage and provides clear targets for engineers, improving alignment and the quality of the final product.

The word "evals" has been stretched to mean many different things: expert-written error analysis, PM-defined test cases, performance benchmarks, and LLM-based judges. This "semantic diffusion" causes confusion. Teams need to be specific about what part of the feedback loop they're discussing instead of using the generic term.

The primary bottleneck in improving AI is no longer data or compute, but the creation of 'evals'—tests that measure a model's capabilities. These evals act as product requirement documents (PRDs) for researchers, defining what success looks like and guiding the training process.

Building a functional AI agent is just the starting point. The real work lies in developing a set of evaluations ("evals") to test if the agent consistently behaves as expected. Without quantifying failures and successes against a standard, you're just guessing, not iteratively improving the agent's performance.

Because PMs deeply understand the customer's job, needs, and alternatives, they are the only ones qualified to write the evaluation criteria for what a successful AI output looks like. This critical task goes beyond technical metrics and is core to the PM's role in the AI era.