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.

If your application isn't live and you lack real user data, you can still perform evals. The best methods are dogfooding and recruiting friends. If that's not possible, use an LLM to simulate user interactions at scale. This generates the necessary traces to begin the crucial error analysis process before launch.

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.

Unlike traditional software with deterministic outputs, generative AI systems require a new paradigm. Chip Huyen calls this "evaluation-driven development," where the focus shifts from writing fixed tests to building robust systems and guidelines for evaluating ambiguous, generative outputs.

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.

The prompts for your "LLM as a judge" evals function as a new form of PRD. They explicitly define the desired behavior, edge cases, and quality standards for your AI agent. Unlike static PRDs, these are living documents, derived from real user data and are constantly, automatically testing if the product meets its requirements.

Comparing AI models based on single, identical prompts is a flawed methodology. A true evaluation involves 'driving' the model through multiple iterations of feedback and correction. This reveals its ability to understand and adapt to your specific intent, which is a far more critical measure of its utility than a single probabilistic output.

Evals shift product development from defining the 'how' to defining the 'what'. By creating quantifiable tests and success criteria, evals act like a modern PRD. This allows an AI model to creatively figure out the implementation while the team focuses on defining the desired outcome through concrete examples.