Evals & Safety
Eval / Evaluation
The systematic process of testing an AI model's performance against a defined set of inputs and expected outputs.
In depth
The systematic process of testing an AI model's performance against a defined set of inputs and expected outputs. Evals measure whether a model is actually good at the task you care about, not just benchmarks. They can be automated (comparing outputs to ground truth) or human-judged (rating quality on a rubric). Running evals before and after changes is how teams catch regressions and validate improvements.
Example
In practice, developers reach for Eval / Evaluation when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Evals & Safety.
FAQ
Common questions
What is Eval / Evaluation?
The systematic process of testing an AI model's performance against a defined set of inputs and expected outputs.
Why does Eval / Evaluation matter for AI developers?
Eval / Evaluation sits in the Evals & Safety part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Eval / Evaluation?
Developers Digest publishes tutorials and videos that cover Evals & Safety topics including Eval / Evaluation. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Evals & Safety
Benchmarks (AI)
Standardized tests that measure model performance on tasks like code generation, math, reasoning, and instruction following.
Evals & Safety
Constitutional AI
An alignment technique developed by Anthropic where an AI model is trained to follow a set of principles (a constitution) that guide its behavior.
Models
Extended Thinking
A Claude feature that gives the model a dedicated thinking phase before producing its visible response.
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