How a 20-Prompt Test Harness Can Help Teams Pick the Right Free AI Model

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Most development teams select AI models based on trending articles or reputation rather than empirical testing, which can lead to poor fit for their specific workloads. A structured evaluation method using 20 prompts drawn from a team's own repository — such as real commits, issues, and past failures — offers a more evidence-based approach to model selection. Each model's output is scored against four mechanical criteria: whether it compiles, stays within length bounds, uses the provided context, and avoids hallucinated APIs. The results are compiled into a decision table that ranks models by total score, removing subjective opinion from the selection process. The harness is designed to be rerun whenever workloads shift, since a model that excels at commit messages may underperform at test generation.

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