AI Evaluation (Evals)
AI evaluation (evals) is the emerging skill of systematically testing and measuring AI model performance. As models become products, evals become the product requirements document. This involves error analysis, creating rubrics, building benchmarks, and developing systematic tests - a critical bottleneck for AI labs and a new core competency for product builders.
The Guide
3 key steps synthesized from 2 experts.
Treat evals as your product requirements
In AI products, the eval suite defines what the product should do. If you can't measure it, you can't improve it. Before building features, define how you'll evaluate success. The eval is the spec - it tells the model (and your team) exactly what 'good' looks like.
Featured guest perspectives
"If the model is the product, then the eval is the product requirement document."— Brendan Foody
Build systematic evaluation workflows
Develop a multi-step process: start with error analysis to understand where the model fails, use open coding to categorize failure modes, create rubrics based on those categories, and build automated tests. This systematic approach replaces gut-feel assessments with rigorous measurement.
Featured guest perspectives
"Both the chief product officers of Anthropic and OpenAI shared that evals are becoming the most important new skill for product builders."— Hamel Husain & Shreya Shankar
Invest in this as a core skill
The heads of product at major AI labs consider evals one of the most important emerging skills. This isn't traditional QA or software testing - it's a new discipline that product builders need to develop. Treat it as a first-class competency worth investing significant time in learning.
Featured guest perspectives
"Both the chief product officers of Anthropic and OpenAI shared that evals are becoming the most important new skill for product builders."— Hamel Husain & Shreya Shankar
Common Mistakes
- Treating AI testing like traditional software testing
- Relying on vibes instead of systematic measurement
- Not investing in eval infrastructure early
- Evaluating only accuracy without considering other dimensions like safety, helpfulness, or style
Signs You're Doing It Well
- You can quantify model performance across multiple dimensions
- You have automated eval suites that run on every model change
- Your product decisions are informed by eval results, not intuition
- You can explain exactly why one model version is better than another
All Guest Perspectives
Deep dive into what all 2 guests shared about ai evaluation (evals).
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."
Brendan Foody
"If the model is the product, then the eval is the product requirement document."
Install This Skill
Add this skill to Claude Code, Cursor, or any AI coding assistant that supports Agent Skills.
Download the skill
Download SKILL.mdAdd to your project
Create a folder in your project root and add the skill file:
.claude/skills/ai-evals/SKILL.mdStart using it
Claude will automatically detect and use the skill when relevant. You can also invoke it directly:
Help me with ai evaluation (evals)Related Skills
Other AI & Technology skills you might find useful.
AI Product Strategy
AI strategy should focus on using algorithms to scale human expertise and judgment rather than just...
View Skill → →Building with LLMs
Using LLMs for text-to-SQL can democratize data access and reduce the burden on data analysts for ad...
View Skill → →Platform Strategy
Platform and ecosystem success comes from identifying 'gardening' opportunities—projects with inhere...
View Skill → →Evaluating New Technology
Be skeptical of 'out-of-the-box' AI solutions for enterprises; real ROI requires a pipeline that acc...
View Skill → →