Table of Contents
Should You Use Haiku or Same Model for AI Prompt Test Cases?
Learn why using the exact same Claude model for test case generation matches production behavior perfectly, outperforming faster or pricier options for accurate prompt evaluation.
Question
You need test cases for your prompt evaluation. You have two options: write them by hand or use Claude to generate them. Which model should you use for generation?
A. The most expensive model available
B. Multiple models combined
C. A faster model like Haiku
D. The same model you’re testing
Answer
D. The same model you’re testing
Explanation
Using the same model you’re testing—such as Claude Sonnet if that’s your production model—for generating test cases ensures the inputs accurately reflect real-world behaviors, edge cases, and failure modes specific to that model’s reasoning patterns, tokenization quirks, and response tendencies.
A faster/cheaper model like Haiku (C) might produce simpler or less representative test cases that miss nuances the target model encounters; expensive models (A) waste resources without fidelity gains; and combining models (B) introduces inconsistencies that dilute evaluation validity. This matching approach, a core prompt engineering best practice, allows reliable simulation of production inputs for robust testing, preventing false positives from mismatched test data.