Q: How much of my development budget should I allocate to evals?
It’s important to recognize that evaluation is part of the development process rather than a distinct line item, similar to how debugging is part of software development.
You should always be doing error analysis. When you discover issues through error analysis, many will be straightforward bugs you’ll fix immediately. These fixes don’t require separate evaluation infrastructure as they’re just part of development.
The decision to build automated evaluators comes down to cost-benefit analysis. If you can catch an error with a simple assertion or regex check, the cost is minimal and probably worth it. But if you need to align an LLM-as-judge evaluator, consider whether the failure mode warrants that investment.
In the projects we’ve worked on, we’ve spent 60-80% of our development time on error analysis and evaluation. Expect most of your effort to go toward understanding failures (i.e. looking at data) rather than building automated checks.
Be wary of optimizing for high eval pass rates. If you’re passing 100% of your evals, you’re likely not challenging your system enough. A 70% pass rate might indicate a more meaningful evaluation that’s actually stress-testing your application. Focus on evals that help you catch real issues, not ones that make your metrics look good.
This article is part of our AI Evals FAQ, a collection of common questions (and answers) about LLM evaluation. View all FAQs or return to the homepage.