Whetstone is a personal "prompt interceptor" system that filters vague user requests before they hit a model like Claude. By applying a custom constraint library to every input, the system forces vague requests into detailed, structured briefs—reducing the ambiguity models typically face when left to guess tone, length, or target audience. While the mechanism works as a pre-execution layer, its claim of producing "better" results rests on the user's personal definitions of quality rather than independent, objective performance data.

This is a practical, if narrow, look at building a personalized workflow layer to fix model output inconsistencies. Skip it unless you need a concrete example of how to structure a prompt-enhancement system, as the actual substance here is brief and relies entirely on the creator's subjective evaluation.