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.

Teams often mistakenly debate between using offline evals or online production monitoring. This is a false choice. Evals are crucial for testing against known failure modes before deployment. Production monitoring is essential for discovering new, unexpected failure patterns from real user interactions. Both are required for a robust feedback loop.

To combat self-inflicted setbacks, HubSpot created a "Pothole Report." When a metric blew up (like support wait times), they identified the leading indicators they missed. These indicators were then added to a comprehensive report, reviewed monthly, to prevent the same issue from recurring.

To move beyond static playbooks, treat your team's ways of working (e.g., meetings, frameworks) as a product. Define the problem they solve, for whom, and what success looks like. This approach allows for public reflection and iterative improvement based on whether the process is achieving its goal.

Don't treat your test dataset as static. Monitor online eval scores in production. When you see poor performance, filter for those failing examples and add them to your offline dataset. This ensures your testing evolves with real-world usage patterns.

The common mistake in building AI evals is jumping straight to writing automated tests. The correct first step is a manual process called "error analysis" or "open coding," where a product expert reviews real user interaction logs to understand what's actually going wrong. This grounds your entire evaluation process in reality.

The most effective product reviews eliminate all abstractions. Forbid presentations, pre-reads, and storytelling. Instead, force the entire review to occur within the actual prototype or live code. This removes narrative bias and forces an assessment of the work as the customer will actually experience it.

Instead of stigmatizing failure, LEGO embeds a formal "After Action Review" (AAR) process into its culture, with reviews happening daily at some level. This structured debrief forces teams to analyze why a project failed and apply those specific learnings across the organization to prevent repeat mistakes.

Solely measuring a team's output fails to capture the health of their collaboration. A more robust assessment includes tracking goal achievement, team psychological safety, role clarity, and the speed of execution. This provides a holistic view of team effectiveness.