Prompt clarity scorer
The single biggest cause of disappointing LLM output is an unclear prompt. Vague verbs, missing constraints, and no stated output format leave the model to guess — and it guesses inconsistently. This scorer rates your prompt on three axes that predict good results: task clarity, constraint specificity, and output definition. It returns a 0 to 100 score and a prioritized list of fixes, all computed locally in your browser.
How it works
The tool tokenizes your prompt and applies a set of heuristics. For task clarity it looks for an explicit action verb (summarize, classify, generate, rewrite) near the start and a defined subject. For specificity it counts concrete signals — numbers, named entities, constraints, and “must/only/never” language — and subtracts points for hedge words like “something,” “good,” or “etc.” For output definition it checks whether you stated a format (JSON, list, table), a length, or gave an example. Each axis produces a sub-score, and the three combine into an overall rating with targeted suggestions for whichever axis is weakest.
Tips and notes
- Lead with the verb. “Summarize the text below in three bullet points” scores far higher than “tell me about this.”
- State the output. Naming a format and length is the fastest single way to raise the score.
- Replace hedge words. Swap “good examples” for “three real-world examples under 20 words each.”
- The score is a guide, not a grade. It catches ambiguity, but you still own whether the request itself is sensible.