AI Evaluation (Evals)

Category: AI & Technology

Source: https://refoundai.com/lenny-skills/s/ai-evals

AI Evaluation (Evals) | Refound AI

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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.

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The Guide 3 key steps synthesized from 2 experts.

1 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.

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"If the model is the product, then the eval is the product requirement document."

— Brendan Foody 2 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 3 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 testingRelying on vibes instead of systematic measurementNot investing in eval infrastructure earlyEvaluating only accuracy without considering other dimensions like safety, helpfulness, or style ✓ Signs You're Doing It Well

You can quantify model performance across multiple dimensionsYou have automated eval suites that run on every model changeYour product decisions are informed by eval results, not intuitionYou can explain exactly why one model version is better than another

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Deep dive into what all 2 guests shared about ai evaluation (evals).

Hamel Husain & Shreya Shankar 1 quote

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"Both the chief product officers of Anthropic and OpenAI shared that evals are becoming the most important new skill for product builders."

View all skills from Hamel Husain & Shreya Shankar →

Brendan Foody 1 quote

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"If the model is the product, then the eval is the product requirement document."

View all skills from Brendan Foody →

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