Prompt Tester
Paste two prompt drafts and see which one is more structurally complete, scored against an eight-point checklist.
A prompt that looks clear on the page can still produce inconsistent output once real, messy inputs hit it. This tool runs your prompt against a small set of deliberately varied test inputs — an edge case, a minimal input, and a typical one — so you can see how it actually behaves before relying on it.
How to use it
Works best with prompts that take some form of variable input, not fully static ones.
More variations surface more edge cases, but take longer to review.
Look for inconsistency in structure or tone between runs, not just factual correctness.
Tips for better results
- Test with a deliberately awkward input, not just a clean one. A prompt that only works on tidy inputs will fail in real use, where inputs are rarely tidy.
- Compare structure across runs, not just content. A prompt producing wildly different formatting between runs is a sign the instructions aren't specific enough.
- Re-test after every edit, even a small one. Small wording changes can shift how a model interprets the whole prompt in ways that aren't obvious from reading it.
Example output
“Testing a customer-support reply prompt across 3 inputs — a simple refund request, an angry multi-issue complaint, and a one-word "help" message — reveals whether the tone instruction holds up under an angry input, not just a calm one.”
TL;DR
A prompt that “feels” better isn’t always structurally better — and eyeballing two drafts side by side rarely tells you which one will actually perform differently with an AI model. The Prompt Tester runs two prompt drafts against the same eight-point checklist and scores each one, so instead of guessing which version is stronger, you can see exactly which structural elements one has that the other is missing.
Why it helps
The checklist looks for the same things a careful prompt writer checks by hand: a defined role, a stated task, an output format, context, constraints, examples, and language specific enough to avoid vague filler words like “some,” “good,” or “things.” Each of these maps to a concrete gap in how a model interprets an instruction — a missing format means the model guesses the shape of the answer, a missing constraint means it guesses the length, and so on.
What the tester deliberately doesn’t do is judge writing quality. Two prompts can score the same on structure and still read very differently — one clear and direct, one clunky but technically complete. That’s on purpose. Structure and style are separate problems, and conflating them is how people end up “fixing” a prompt’s wording when the actual issue was a missing constraint the whole time.
What the checklist actually checks
Each of the eight checks targets a specific, common failure mode in prompt writing:
- Role or persona — does the prompt tell the model who to be before it tells it what to do?
- Clear task up front — does it open with a concrete action verb (write, summarize, compare) rather than background information?
- Output format — is there any indication of what shape the answer should take?
- Context or background — is there enough surrounding information for the model to calibrate its answer, or is it working blind?
- Length or constraints — is there a stated limit, whether that’s a word count, a rule to follow, or a hard requirement?
- Examples — does the prompt show what “done” looks like, rather than just describing it?
- Specific rather than vague language — how many filler words like “some,” “nice,” or “stuff” made it into the final draft?
- Reasonable length — is the prompt long enough to actually carry the information above, or is it too thin to do any of this?
A typical before-and-after
Someone testing “write something about our new product” against a revised version — “You are a product marketer. Write a 100-word announcement for our new running shoe, aimed at existing customers, in an upbeat but not salesy tone” — will usually see the second draft clear five or six checks the first draft failed entirely. The score isn’t the point; the list of which specific checks flipped from failing to passing is, because that list is a direct to-do list for improving the weaker draft.
Who this is for
Anyone iterating on a prompt they’ll reuse regularly benefits from this — content teams standardizing a prompt template across a company, developers refining a system prompt before it goes into production, or anyone who’s rewritten the same prompt five times and wants an objective way to tell if version five is actually better than version two. It’s also useful paired with the Prompt Optimizer: test a draft here first, then run it through the optimizer to generate a scaffold for whatever’s still missing.
FAQ
Does this tool call an AI model to compare the prompts?
No. The comparison is a rule-based checklist that runs entirely in your browser — it checks structure, not writing quality.
What does the score actually measure?
How many of eight common prompt-writing checks your prompt passes — for example, whether it states a format, gives context, or avoids vague words like “some” or “good.”
Can I compare more than two prompts?
Run the comparison again with a new pair, keeping the stronger prompt from the previous round. There’s no limit to how many rounds you run.
Why did my prompt score low even though it reads well?
The checklist measures structure, not prose quality. A well-written sentence with no stated format or constraint will still score low on those specific checks — that’s a real gap, not a false positive.
Should I aim for the same score on both prompts?
No — the goal is the highest score that matches what your task actually needs. A short factual-lookup prompt doesn’t need an Examples field the way a creative-writing prompt does.
