Resource
Is My Prompt Clear Enough?
Answer the question directly: a clarity score plus the specific reasons a prompt reads as unclear — vague terms, unstated objective, missing specifics.
Find Vague Instructions in a Prompt
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.
How to use this resource
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Paste the prompt
One that leans on quality words like "good" or "engaging".
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See each flagged term
Subjective words that read as instruction but specify nothing.
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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
FAQ
Will this tool swap the vague words for specific ones, or just flag them?
It finds them, it doesn't replace them. The report lists each subjective quality term — here six of them: high-quality, engaging, compelling, professional, clean, appropriate — and marks the prompt as a read-only diagnosis. For actually swapping vague words for specific ones the resource points to the Prompt Rewriter; this surface just makes the specificity gap visible.
How can a prompt be full of words but still score low on specificity?
That's exactly what the report exposes. This example scores Specificity 13/100 with only 2 of 6 concrete anchors present (number, length limit, format, audience, examples, criteria) despite reading like instruction — because words like "professional" and "engaging" feel like direction yet specify nothing measurable. Listing them side by side with the low anchor count is the whole point.
More resources from Prompt Readability Checker
Resource
Prompt Readability Checker
Diagnose a prompt before you send it: explainable scores for clarity, ambiguity, specificity, and structure, with observations that name where it is vague.
Resource
Detect Ambiguity in a Prompt
Surface the ambiguity a model will exploit: vague quantities, hedges, undefined time, open-ended lists, and conflicting instructions — flagged, not fixed.
Resources that pair well
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Prompt Cleanup Examples (Before & After)
A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.
Resource
Fix a Vague Prompt
'Make it good', 'be detailed', 'keep it interesting' — vague prompts get vague output. The fix is mechanical: replace every fuzzy word with a checkable instruction.
Resource
Agent Instruction Prompt Formatter
Formats fuzzy agent instructions into a structured prompt with objective, available tools, constraints, success criteria, and failure handling.
Related tools
Tool
Prompt Readability Checker
Diagnose a prompt's clarity before you send it — explainable scores for clarity, ambiguity, specificity, and structure. Read-only; it never rewrites.
Guides for this resource
Guide
Improve a Weak Prompt Without Starting Over
A weak prompt usually isn't all wrong — it's a good ask with two or three decisions left unmade. Here's how to diagnose why it underperforms, keep the parts that work, and patch the weak ones, instead of deleting it and starting from a blank line.