A long prompt can carry a dozen instructions — must include a disclaimer, never mention competitors, keep it under 100 words, always end with a CTA. Models miss some of these surprisingly often. This checker extracts the instructions from your prompt and audits the output against each, so you can see compliance at a glance instead of re-reading both texts line by line.
Why models miss instructions
The longer and more complex a prompt, the harder it is for a model to track every constraint simultaneously. Research and practical experience both show that instructions buried in the middle of a long prompt tend to be followed less reliably than those near the beginning or end — a phenomenon sometimes called the “lost in the middle” effect. Instructions that conflict with each other, or that require counting (word limits, numbered lists) are also consistently under-followed.
Running a quick compliance check after every generation — especially for templated or regulated outputs — is the fastest way to catch these before sending.
How it works
The tool splits your prompt into sentences and flags any that contain imperative cue words — must, should, do not, never, always, only — plus explicit length or format constraints. Each becomes a checklist item. For instructions it can verify mechanically (a banned word, a required phrase, a word limit) it runs the check against your output and shows a verdict with the reason. Instructions it cannot judge automatically are still listed so your checklist stays complete. Everything runs locally in your browser.
What it can and cannot verify
- Auto-checkable: negative constraints (“do not mention X”), positive requirements (“must include Y”), and numeric limits (“under 100 words”).
- Manual only: subjective or stylistic instructions (“be concise”, “sound friendly”) — surfaced for you to judge.
Practical example
Suppose your prompt contains:
- “Do not mention any competitor brands.”
- “Must include a disclaimer at the end.”
- “Keep the response under 150 words.”
- “Always use a friendly, casual tone.”
The checker would auto-verify the first three: it searches for known competitor names (if you listed them), checks for the word “disclaimer,” and counts the output’s words. The fourth instruction — tone — goes on your manual review list because tone is subjective. The result is a checklist with three auto-verdicts and one prompt to read yourself, rather than re-reading both texts from scratch.
Tips
- Write prompt instructions as clear, separate sentences so the extractor catches each one.
- Treat every verdict as a prompt to look, not a final ruling — heuristics produce both false positives and false negatives.
- Re-run after editing the prompt to confirm new constraints were actually picked up.
- For high-stakes outputs (legal, compliance, regulated copy), pair this with a human sign-off on every instruction, especially the manual-only ones.
- Place your most important constraints at the beginning and end of the prompt — not buried in the middle — to improve the model’s own compliance before you even run a check.