ai-evals
Systematic evaluation framework for AI products using practitioner-driven methodologies.
- Guides users through understanding what "good" looks like, designing rubrics and test cases, and implementing scoring criteria aligned with actual user needs
- Emphasizes manual review and error analysis as prerequisites to building meaningful evals, with structured workflows for clustering failure patterns
- Flags common pitfalls including vague criteria, LLM-as-judge without validation, and Likert scales; recommends binary Pass/Fail decisions instead
- Positions evals as core product specifications rather than optional quality checks, essential for product builders and non-ML roles alike
AI Evals
Help the user create systematic evaluations for AI products using insights from AI practitioners.
How to Help
When the user asks for help with AI evals:
- 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
Core Principles
Evals are the new PRD
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.
Evals are a core product skill
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.
The workflow matters
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.
Questions to Help Users
- "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 better or worse?"
- "Are you measuring what users actually care about?"
- "Have you manually reviewed enough outputs to understand failure patterns?"
Common Mistakes to Flag
- Skipping manual review - You can't write good evals without first understanding failure patterns through manual trace analysis
- Using vague criteria - "The output should be good" isn't an eval; you need specific, measurable criteria
- LLM-as-judge without validation - If using an LLM to judge, you must validate that judge against human experts
- Likert scales over binary - Force Pass/Fail decisions; 1-5 scales produce meaningless averages
Deep Dive
For all 2 insights from 2 guests, see references/guest-insights.md
Related Skills
- Building with LLMs
- AI Product Strategy
- Evaluating New Technology