This guide to how to evaluate an ai model for your own use case explains the core concepts, practical decisions, and common mistakes to watch for. Use the steps below as a starting point, then verify details against the tools and requirements in your own workflow.
Define success and failure
Write acceptance criteria for accuracy, format, latency, cost, privacy, and refusal behavior. Identify which errors are tolerable and which require a human decision. Keep the criteria fixed while comparing candidates.
Build a representative test set
Include typical requests, difficult examples, ambiguous inputs, edge cases, and cases where the model should ask a question or refuse. Use the same inputs and settings for each candidate, and keep examples separate from prompt development when possible.
Review results and repeat
Score outputs against a rubric, verify factual claims, and inspect failures by type. Re-run the evaluation when prompts, models, retrieval data, or provider settings change. Store versions so results can be reproduced later.
Practical checklist
Choose the model that meets the requirements at an acceptable cost and risk, not simply the one with the highest public score.
Editorial note: This educational guide explains general methods and does not claim independent product testing. Check official documentation for current features, pricing, and terms before making decisions.
