ai-evals
The ai-evals skill guides users through creating systematic evaluations for AI products and features. Use it when designing rubrics, building test cases, measuring model quality, analyzing failure patterns, or establishing criteria for what constitutes successful AI output. It emphasizes that evals function as product requirements for AI systems and incorporates frameworks from AI practitioners on effective evaluation workflows, common pitfalls, and the importance of manual review before automation.
git clone --depth 1 https://github.com/RefoundAI/lenny-skills /tmp/ai-evals && cp -r /tmp/ai-evals/skills/ai-evals ~/.claude/skills/ai-evalsSKILL.md
# 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: 1. **Understand what they're evaluating** - Ask what AI feature or model they're testing and what "good" looks like 2. **Help design the eval approach** - Suggest rubrics, test cases, and measurement methods 3. **Guide implementation** - Help them think through edge cases, scoring criteria, and iteration cycles 4. **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
Help users define AI product strategy. Use when someone is building an AI product, deciding where to apply AI in their product, planning an AI roadmap, evaluating build vs buy for AI capabilities, or figuring out how to integrate AI into existing products.
Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.
Help users apply behavioral science to product design. Use when someone is designing for habit formation, reducing friction, applying psychology to UX, increasing retention through behavioral principles, or using nudges to influence user behavior.
Help users craft compelling brand narratives. Use when someone is defining brand strategy, writing company positioning, creating pitch narratives, developing messaging frameworks, or trying to make their company story more memorable.
Help users get promoted at work. Use when someone is preparing for a promotion conversation, building their case for advancement, trying to understand what's blocking their promotion, or figuring out how to get to the next level in their career.
Help users build and scale their sales organization. Use when someone is hiring their first salespeople, deciding when to bring on sales leadership, structuring sales compensation, or transitioning from founder-led sales.
Help users build and maintain strong team culture. Use when someone is defining team values, creating psychological safety, onboarding to a new team, navigating cultural change, or building distributed team norms.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.