Audit a dataset for mixed units before analysis
Finds unit mismatches and proposes reversible conversions.
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Turn claims, files, screenshots and risky offers into answers you can inspect. These prompts show what AI can and cannot verify.
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These short prompts work in a chat you already use. For personal questions, the AI should say what it actually knows, not pretend it can see your whole life.
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Finds unit mismatches and proposes reversible conversions.
Designs suppression and aggregation rules for sparse data.
Separates signup, first use and return events before calculating retention.
Separates observations, quotes and outcomes in a reviewable recap.
Uses paired cases to clarify a real rule and its exceptions.
Captures state, evidence and safe next actions without overclaiming completion.
Produces a concise suitability card that helps buyers self-select.
Structures a respectful submission around lived effect and cited evidence.
Maintains one verified fact core across staff, customers and partners.
Explains setup, small change scope and review expectations with a test path.
Turns sourced customer questions into concise answers and open issues.
Preserves key relationships through responsive layout and text alternatives.
Offers readable names and optional contact modes without forced disclosure.
Creates concise labels anchored to real controls and manufacturer guidance.
Prioritises the action, date and location for print or phone.
Specifies animated and static states that carry the same information.
Makes disposal and reuse instructions specific to actual materials.
Maps decision points, sign placement and plain-language wording.
Creates a small comprehension study and revision rule for an icon.
Creates evidence-based checkpoints for long student projects.
Lets learners examine tradeoffs without pretending the exercise is official.
Pairs symbolic steps with a visual model and a reverse check.
Creates a teach-back routine that does not shame the listener.
Builds a learning path backwards from a concrete task.
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