AI Dictionary › Prompting
Calibrazione del Prompt
Prompt calibration is the iterative process of adjusting a prompt's phrasing, examples or constraints by systematically comparing the outputs obtained against an expected reference result, until the model's response converges stably toward the desired quality.
The process involves defining clear criteria for what constitutes a correct or adequate response, running the prompt on a set of test cases, comparing the results against the established criteria, and modifying the prompt, adding an example, clarifying an ambiguous instruction, fixing a poorly worded constraint, to reduce the observed gap. The cycle repeats until results stabilize within an acceptable quality range.
This technique is central when developing prompts intended for repeated, large-scale use, for example in a product that automatically generates responses for end users: the prompt is calibrated by testing it against dozens or hundreds of representative cases, progressively correcting the most frequent error patterns before release.
The term is borrowed from the language of measurement and scientific instruments, where calibrating means aligning an instrument to a known reference, an image that fits well the process of progressively refining a prompt toward an expected and verifiable behavior.
Grace shows you where your prompt deviates from the expected result in the scenario, so you can calibrate it step by step.
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