Prompt instruction conflict checker
One of the quietest causes of unreliable LLM output is a prompt that contradicts itself. “Be concise” sits two lines above “explain your reasoning in full.” “Use a formal tone” competes with “keep it casual and friendly.” The model can’t satisfy both, so it silently picks one — and you get inconsistent results. The prompt instruction conflict checker scans for these contradictions using rule-based heuristics, right in your browser.
How it works
The checker holds a library of opposing instruction pairs, each defined by sets of trigger phrases. For every pair it looks for evidence of both sides in your prompt:
- Brevity vs. comprehensiveness — “be brief / concise / short” against “be thorough / comprehensive / detailed”.
- Formal vs. casual tone — “formal / professional” against “casual / friendly / conversational”.
- Strict format vs. free-form — “only return JSON / exact format” against “feel free / be creative / open-ended”.
- Certainty vs. hedging — “be definitive / no caveats” against “note uncertainty / add disclaimers”.
- Persona conflicts — “act as an expert” against “explain like I’m five”.
When both sides of a pair appear, it flags the tension and shows the triggering phrases so you can see exactly what clashed.
Tips and examples
Treat flagged conflicts as a prompt to make a decision, not as errors. Sometimes you genuinely want a nuanced balance — in that case, add an explicit precedence rule: “Prefer brevity; only expand when the answer would otherwise be wrong.” That single line tells the model how to resolve the tension instead of guessing. Run the checker after every major prompt edit; conflicts often creep in when you stack new instructions on top of old ones without re-reading the whole thing.