Evals
Also: AI evaluation
Systematic tests for measuring how well Claude performs on your specific tasks — the AI equivalent of unit tests in software development. Instead of just "trying it out and seeing if it seems right," evals give you a measurable score: "Claude answered 87 out of 100 test cases correctly." They let you compare models, catch regressions when you change prompts, and build confidence before deploying changes. Most teams skip evals early on — and regret it when something silently breaks in production.
In practice
You've updated your system prompt and want to know if it's actually better. You run both versions on 200 test cases and compare: did accuracy go up? Did the format improve? Did anything break? Those tests are evals — the way you measure whether a change to your Claude setup made things better or worse before shipping it.
Related concepts
Where Evals shows up
8 articlesWhat to measure, how to structure test cases, and how to run evals in CI so that prompt changes and model updates don't silently break your product.
Claude API calls are invisible unless you instrument them. Here is the logging structure, the metrics that actually matter, what Anthropic rate limiting looks like in practice, and the alert thresholds worth setting.
Most eval suites test what was easy to write, not what matters most. A structured audit finds the gaps before production does — coverage blind spots, flaky assertions, and the failure modes you forgot to cover.
Most teams go live on gut feel and find out six weeks later that Claude has been quietly giving wrong answers. Here's how to know before that happens — without being an engineer.
Evals are the testing framework for AI — and they work differently from software tests. You're not checking for correct answers. You're measuring behavior across a range of realistic situations.
Most AI pilots don't fail because the AI wasn't good enough. They fail for three very predictable reasons — none of which are technical.
Most AI pilots succeed technically and fail politically. The evidence exists — it just wasn't collected in a way anyone can act on. Here's how to design a pilot that produces results your organization will actually use.
Most AI rollout evaluations are either too vague ("the team likes it") or too technical (automated test suites that miss what users actually care about). Here's what works.