Pick a Free AI Model by Score, Not by Reputation: A 20-Prompt Harness
Model selection is the new dependency pinning. You would not add a library to your lockfile because a blog post praised it; you would run its tests against your own code first. Most teams do the opposite with AI models: they pick one from a trending article, configure it once, and never re-score it. A 20-prompt harness turns that decision back into evidence. Here is the concrete situation I am…
Choosing an AI model based solely on its reputation is no longer an effective strategy. Instead, a 20-prompt harness provides a more reliable approach. By selecting models based on evidence from a set of prompts defined by actual tasks in your project, you can make a more informed decision. The harness involves writing 20 prompts that reflect real workload scenarios, then evaluating each prompt against every candidate model.
The results are scored using a rubric that assesses code compilation, length, contextual relevance, and authenticity. The scores are compiled into a decision table, ranking models based on their performance. This method requires a small number of representative prompts and should be re-evaluated when the workload changes. It's important to note that this approach is not a catch-all solution and has limitations, such as API cost considerations and the inability to fully validate correctness.
However, it does provide a systematic way to compare models based on concrete evidence, rather than on reputation alone.
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