Prompt catalog
Browse reusable AI prompts by topic, then open any prompt to copy and adapt it for your workflow.
375 prompts · page 8 of 42
Describe your own product, name three to five specific competitors, and pick the dimensions to compare on — features, pricing, UX, marketing — for a structured side-by-side competitive read. For each competitor it covers strengths, weaknesses, market positioning, pricing strategy, themes from customer reviews, and marketing channels, ending with your own competitive advantages and the gaps you still need to close. Worth rerunning quarterly as positioning shifts.
Describe the research question, the two groups being compared with their sample sizes, the outcome variable, and the significance level, and this prompt runs a complete hypothesis test end to end. - States the null and alternative hypotheses in plain language - Checks the chosen test's assumptions (normality, variance, independence) - Selects and justifies the right test, reporting the statistic, p-value - Reports a confidence interval alongside the p-value - Computes an effect size and comments on practical significance - Delivers the full Python/R workflow as code
Paste in column names, types, and roughly 20 sample rows along with the role you're framing findings for — a product manager, a CFO — and this ready-made EDA prompt returns summary statistics for every numeric column, value counts for categorical columns, a missing-value breakdown, distribution shapes for key variables, the five most interesting anomalies, and ten business-relevant questions the data could answer, ranked by value. A solid default first pass on any dataset you haven't explored yet.
Describe what each row represents, the columns most relevant for grouping, and the business decisions the segments should inform, and this prompt finds meaningful groups three ways at once. It runs rule-based segmentation from business logic, statistical clustering with an optimal K, and behavioral (recency/frequency/monetary) segmentation where time-series data exists, then profiles each segment, names it in plain language, and pairs it with one actionable recommendation plus the Python code behind it.
List the current messy column names, the target clean names, and your formatting rules for dates, phone numbers, and category mappings. - Generates a runnable pandas script standardizing dates and currency - Splits addresses into street, city, state, and zip - Splits full names into first_name and last_name, formats phone numbers - Ends with a before/after validation summary of row counts and samples
Paste in a raw interview transcript and this prompt produces a fast first-pass summary before any formal coding work begins. It pulls out 3-5 main themes discussed, 3-5 verbatim key quotes from the participant, a read on the participant's overall stance toward the research topic, and any unusual or unexpected perspectives that don't fit neatly into the theme bullets — useful for quickly deciding whether a transcript warrants a deeper coding pass.
Give the specific decision or question this summary needs to inform, plus the interview transcript, and get back a 200-word-or-shorter condensation. Prioritizes actionable findings tied directly to that decision over vague generalities, and favors concrete specifics — useful when a stakeholder needs the gist of an interview in under a minute of reading.
Name the research topic, the sources to draw on, the focus areas you need covered, and the intended audience, and this prompt turns a stack of research into one readable summary. - A 3-5 sentence executive summary - Key findings as bullets, plus the methodologies behind them - Conflicting viewpoints and gaps in the research, flagged explicitly - Practical implications, citations, and further-reading suggestions
Paste the raw statistical results — p-values, confidence intervals, effect sizes, coefficients — along with who you're presenting to, and this prompt converts them into language an executive audience can actually use. It produces a 3-sentence jargon-free executive summary, a "so what" section spelling out what should change because of the numbers, a plain-language confidence statement instead of "p < 0.05," a magnitude analogy that makes the effect intuitive, honest caveats, and a description of one chart that would communicate the finding.