Evaluating an AI model means testing it against tasks that look like your actual use case, not just checking how it scores on a public leaderboard. Public benchmarks tell you how a model performs on standardized tests; an eval you build yourself tells you whether that model will actually work for the specific job you’re about to hand it.
What’s the Difference Between a Benchmark and an Eval?
A benchmark is a fixed, public test—like MMLU or HumanEval—that lets you compare models against each other on a standardized task. An eval, in the sense Anthropic and OpenAI use the term, is a test suite built around your own use case: your prompts, your data, your definition of a correct answer. Anthropic’s engineering guide to demystifying evals for AI agents makes the point directly—public benchmarks are a starting point for choosing a model, but they rarely predict how well that model will handle your specific workflow, especially once tool use and multi-step reasoning are involved.
How Do You Build a Simple Eval From Scratch?
OpenAI’s evaluation best practices guide outlines a workable starting structure: collect a set of real or representative inputs, define what a correct or acceptable output looks like for each one, run the model against the set, and score the results—either with an automated grader, a rules-based check, or human review. The open-source openai/evals framework was built specifically to make this repeatable rather than a one-off spreadsheet exercise.
- Start with real failures. Pull 20-50 cases where an existing model got something wrong, and use those as your first test set—they’re more revealing than random samples.
- Write down what “correct” means before you test. A grading rubric agreed on in advance prevents you from unconsciously moving the goalposts once you see the outputs.
- Test the whole task, not just the model’s raw answer. If the model calls tools or retrieves data, the eval should catch failures anywhere in that chain, not just in the final sentence.
- Re-run the same eval when you change models or prompts. This turns model selection into a comparison you can actually defend, not a gut feeling.
What Should You Test Beyond Raw Accuracy?
Accuracy on the happy path is only part of the picture. A thorough eval also checks latency and cost per task, how the model behaves on edge cases and ambiguous inputs, and how it responds when it’s fed adversarial or malformed input—a category of testing closely related to the safety checks covered in what an AI guardrail actually does. If your application routes tasks across multiple models, your evals should also confirm the routing logic sends each task to a model that actually performs well on it, which is the whole premise behind AI model routers.
How Often Should You Re-Evaluate a Model?
Re-run your eval suite whenever a provider ships a new model version, whenever you change your prompts in a way that could shift behavior, and on a regular cadence even if nothing changes—model providers periodically update models behind the same API endpoint, and a model that passed your eval last quarter isn’t guaranteed to behave identically today.
Frequently Asked Questions
Do I need a large test set to get useful results?
No. A focused set of 20-50 realistic cases, especially ones drawn from actual past failures, will usually surface more useful signal than a large but generic test set. You can grow the set over time as you find new edge cases.
Can I use an AI model to grade another AI model’s answers?
Yes, this is a common pattern called LLM-as-judge, and both OpenAI’s and Anthropic’s evaluation guidance describe it as a legitimate approach for subjective tasks like tone or helpfulness. It works best when paired with a clear rubric and spot-checked periodically against human judgment.
Are public benchmarks like MMLU still worth looking at?
They’re a reasonable first filter when narrowing down which models to test further, but they shouldn’t be the final word. A model that tops a general benchmark can still underperform on your specific task compared to a model that scores lower overall.
