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.

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 reliable AI agents requires a developer mindset shift. The most critical task is not writing the agent's code but creating robust evaluations ('evals') that define and verify the desired business outcome. This makes a test-driven development approach non-negotiable for enterprise AI.

A "vibe check" is simply using your brain as a scoring function to intuit if an AI output is good. This aligns with the "do things that don't scale" startup principle and is a necessary first step before building more robust, scalable evaluation systems.

Don't start building evaluations from a blank slate. Use an AI agent to analyze your production traces and automatically generate a baseline 'vibe eval.' This initial evaluation won't be perfect, but it provides a starting point for refinement and accelerates the improvement loop.

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 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.

Shift the AI development process by starting with workshops for the people who will live with the system, not just those who pay for it. The primary goal is to translate their stories and needs into tangible checks for fairness and feedback before focusing on technical metrics like accuracy and speed.

Instead of seeking a "magical system" for AI quality, the most effective starting point is a manual process called error analysis. This involves spending a few hours reading through ~100 random user interactions, taking simple notes on failures, and then categorizing those notes to identify the most common problems.