Words like "good", "engaging", and "professional" feel like instructions but specify nothing. Find them so you know where the prompt leaves things open.
Overview
Subjective quality words are the most common prompt trap: "make it good", "keep it professional", "something engaging" sound like direction but tell the model nothing measurable. This loads a prompt built almost entirely from them and flags each one, showing how a request can be full of words yet empty of specifics. Seeing them listed is the point — the prompt reads as instruction but scores low on specificity. It finds the vague terms; it does not replace them.
Workflow
1
Paste the prompt
One that leans on quality words like "good" or "engaging".
2
See each flagged term
Subjective words that read as instruction but specify nothing.
3
Spot the specificity gap
Full of words, low on measurable direction.
Why This Works
Subjective quality words feel like direction but specify nothing
Listing them exposes a prompt that is wordy yet vague
It finds the vague terms without replacing them for you
Best for
Catching subjective quality words
Spotting wordy-but-vague prompts
Raising specificity awareness
Not for
Swapping vague words for specific ones — that's the Prompt Rewriter
Removing filler — that's the Prompt Cleaner
Use cases
Catching subjective quality words
Spotting wordy-but-vague prompts
Raising specificity awareness
Tip: Save time by exploring related resources and tools that integrate with this workflow.
Found a bug, have a suggestion, or want to report something confusing? Send a short note.
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