ai-evals

Help the user create systematic evaluations for AI products using insights from AI practitioners. When the user asks for help with AI evals: Brendan Foody: "If the model is the product, then the eval is the product requirement document." Evals define what success looks like in AI products—they're not optional quality checks, they're core specifications. Hamel Husain & Shreya Shankar: "Both the chief product officers of Anthropic and OpenAI shared that evals are becoming the most important new skill for product builders." This isn't just for ML engineers—product people need to master this. Building good evals involves error analysis, open coding (writing down what's wrong), clustering failure patterns, and creating rubrics. It's a systematic process, not a one-time test. For all 2 insights from 2 guests, see references/guest-insights.md - Understand what they're evaluating - Ask what AI feature or model they're testing and what "good" looks like - Help design the eval approach - Suggest rubrics, test cases, and measurement methods - Guide implementation - Help them think through edge cases, scoring criteria, and iteration cycles - Connect to product requirements - Ensure evals align with actual user needs, not just technical metrics - "What does 'good' look like for this AI output?" - "What are the most common failure modes you've seen?" - "How will you know if the model got...