AI Prompt Analyzer — Score, Debug & Improve
Paste any AI prompt and get a quality score, weakness analysis, and specific improvement suggestions.
Free · Quality score + feedback · No signup
Worked example
What an actual prompt analysis looks like
Below is one prompt run through the analyser, showing what the input looks like, what the analyser flags as working, and what it suggests adding. The three sections are parts of the same analysis — read them together, not as separate examples.
A misty mountain at sunrise, golden light, cinematic, wide angle, Fujifilm
Above the “ship-it” threshold for most image models — well-formed prompt with one or two missing layers.
- Subject named
- Lighting specified
- Mood implied
- Composition stated
- Camera / lens hinted
- Style reference present
Time period or era · color palette spec · specific artist reference or art movement.
Remove: none — this is a well-structured prompt.
How to Use This Tool
Prompt to analysis in 3 steps
Why it works
Why prompt analysis improves output quality
Most prompts fail for specific, identifiable reasons. Knowing why saves compute and time.
Part of the prompt tools suite — after analysis use the prompt enhancer.
Prompt quality problems are specific and identifiable. This tool diagnoses them and tells you exactly what to fix.
For when a prompt is producing weak or inconsistent output and you want a structured opinion on why before you start rewriting. The analyser scores the prompt on dimensions like specificity, model-fit, and stylistic clarity, with concrete suggestions per weakness.
FAQ
Frequently asked questions
Related tools
Continue your workflow
How to read a prompt analyser score
A prompt analyser scores a prompt across multiple dimensions — specificity, model fit, stylistic clarity, length appropriateness — and returns a numerical rating with notes on what to fix. The score is most useful when you treat it as diagnosis, not a verdict. A 7.5 prompt that produces the image you want is a successful prompt; a 9.5 prompt that produces nothing close to your intent is a failure. Below is a short editorial primer on how to read the score and when to ignore it.
What scoring a prompt actually means
Prompt analysers do not predict output quality. They cannot — output quality depends on the model, the seed, the parameters, and a hundred small things the analyser does not see. What they predict is whether the prompt has the structural elements that prompts producing good output usually have. That is a related but different thing.
The implication: a high-scoring prompt is more likely to produce good output, but is not guaranteed to. A low-scoring prompt is more likely to fail, but might still hit if the missing element is one the model fills in well by default. Treat the score as a probability adjustment, not a quality stamp.
The most useful information from an analyser is not the overall score but the per-dimension breakdown. The overall score tells you the prompt has a problem; the dimension breakdown tells you which problem. Acting on the dimension is much faster than rewriting the prompt to chase a higher overall score.
The four dimensions a good analyser checks
The categories vary slightly by tool, but most analysers cover some version of these four.
Specificity. Does the prompt describe what you want, or what you generally want? “A portrait of a woman” is generic; “A close-up portrait of a woman in her 30s, side-lit by a window, soft warm tones” is specific. Low specificity scores mean the model has too many degrees of freedom and will fill in defaults that may not be what you want. The fix is to add one specific detail, then regenerate. Adding too many details overshoots — see the next dimension.
Model fit. Is the prompt’s shape appropriate for the model you are using? A Midjourney-style comma-separated prompt running on Flux scores low on model fit even if it scores high on specificity. The fix is to restructure for the target model — usually a job for the prompt rewriter rather than for an additive edit.
Stylistic clarity. Does the prompt commit to a style, or does it list multiple competing styles? “Cinematic, anime, watercolour” in the same prompt confuses the model — these styles are mutually exclusive in the model’s training. Low stylistic clarity scores almost always mean the prompt has competing modifiers; the fix is to delete two of them, not to add anything.
Length appropriateness. Is the prompt the right length for the target model? Nano Banana wants short prompts; Flux wants long ones. SDXL handles either but rewards weighted tokens at moderate length. A good analyser scores length relative to the named target, not against an absolute scale.
When the score is misleading
Three cases where a score is not telling you what it looks like it is telling you.
The score is high but the output is wrong. This usually means your prompt is well-formed for what it says, but what it says is not what you actually want. A 9-out-of-10 prompt that produces “A perfect rendering of the wrong subject” is a successful prompt with a flawed brief. The fix is not in the prompt; it is in the planning step before the prompt. Rewrite what you are asking for, not how you are asking for it.
The score is low but the output is fine. This usually happens when the prompt is short and the model has good defaults for whatever you are generating. A 4-out-of-10 prompt “Sunset over a mountain lake” can produce excellent output on Midjourney v6 because the model has seen thousands of similar compositions. If the output is what you want, you do not need to chase a higher score.
The score swings on rewrites that do not change meaning. Most analysers are sensitive to surface form — they reward weighted tokens, parameters, and quality tags. A prompt rewritten to add (masterpiece:1.2) at the front will often score noticeably higher even though the model’s output barely changes. Treat large score jumps from cosmetic edits as suspicious.
How to use the analyser inside a workflow
The analyser is most useful as the first step in an iteration loop, not as a one-off check. The loop:
1. Run the prompt through the analyser. Read the dimension breakdown, ignore the overall score for the moment.
2. Apply the suggested fix for the lowest-scoring dimension only. Do not try to fix multiple dimensions at once. Fixing one usually shifts the others, and you want to know which fix made the difference.
3. Generate the image. If the output is what you want, stop. The score does not need to be high; the output needs to be right.
4. If the output is still wrong, look at the next-lowest dimension. Avoid jumping to the highest-leverage looking suggestion — fix the lowest score first because the model is most sensitive to whatever the analyser flagged hardest.
Two or three iterations of this loop is usually enough to get a working prompt. If you are still missing after three iterations, the problem is probably in the brief, not the prompt — go back to step zero and rewrite what you are trying to produce.
When to skip the analyser entirely
Three cases where running the analyser is not the right move.
Your prompt is clearly too short or clearly too long. You do not need a tool to tell you a five-word prompt is underspecified or a 300-word prompt is bloated. Skip directly to the expander for short prompts or the shortener for long ones.
Your prompt produces inconsistent output across runs. Inconsistency usually means the prompt has internal ambiguity, which the analyser may not flag. The right move is the rewriter, which restructures the prompt to remove the ambiguity.
You are translating a working prompt to a new model. A working prompt does not need diagnosis. It needs format adaptation — the rewriter or the model-specific generator on the image-to-prompt generator hub.