Prompt catalog
Browse reusable AI prompts by topic, then open any prompt to copy and adapt it for your workflow.
375 prompts · page 3 of 42
Takes a product's current price, cost structure, competitor pricing, and target margin, then works out whether the price is actually defensible. Covers pricing model options, price elasticity, psychological pricing tactics, competitive positioning, and value-based pricing opportunities, closing with a discounting strategy and a recommendation checked against the target margin.
Reads a two-variant test — descriptions, results, sample size, and duration — and delivers a fast verdict: statistical significance, which variant won, confidence level, key learnings, and a rollout recommendation with suggested follow-up tests. A lighter-weight companion to the rigorous version, meant for everyday test readouts rather than high-stakes ship decisions.
A statistics-first review of an A/B test that goes past a single p-value before you ship the result. - Inputs: control and treatment group results, primary metric, test duration, and optional segments - Checks: post-hoc power, statistical significance with confidence interval, absolute and relative effect size - Flags: novelty-effect risk, sample ratio mismatch, peeking problems, and multiple-comparison issues - Output: a ship/kill/keep-testing call with the reasoning behind it
Feeds churn data, customer base size, churn rate, and revenue impact into an analysis of what's actually driving customers to leave. Surfaces churn triggers, the segments most at risk, retention patterns, and lifetime-value impact, then turns those into prevention strategies, win-back campaign ideas, and the success metrics to track — useful for retention teams building a response plan, not just a diagnosis.
Goes past a raw correlation matrix to flag the relationships in a dataset that actually matter before modeling starts. - Inputs: key variables of interest plus dataset columns, types, and row count - Analysis: correlation matrix, non-obvious pairings, multicollinearity risk, categorical-to-numeric associations, and time-lagged correlations - Warns: spurious correlations that look causal but aren't - Output: plain-language findings plus runnable Python code
Scans an industry over a chosen timeframe and data sources to surface major trend shifts, emerging patterns, and disruption signals, then covers technology impacts, consumer-behavior changes, and regulatory shifts before producing evidence-backed predictions for the next two to five years. Useful for strategy and planning teams who need a forward-looking read rather than a snapshot of current competitors.
Turns raw survey responses — respondent count, key questions, and demographic breakdown — into a findings summary right after a survey closes. Produces the top five findings, sentiment analysis, demographic differences, and unexpected insights, plus actionable recommendations, quote-worthy responses to pull into a deck, and suggested chart types for presenting the results.
Applies an existing qualitative coding framework to a new interview transcript instead of building codes from scratch. - Inputs: a set of codes with definitions, plus the transcript text - For each code: locates every relevant passage and quotes it directly - Fits: deductive coding work where the framework is already established - Pairs with a codebook-matching prompt when uncoded passages also need catching
Audits a dataset's health before analysis starts, using its columns, row count, source, time range, and a sample of rows to spot missing values, type mismatches, likely outliers, duplicate rows, and inconsistent categorical entries. Returns a 1-10 quality score with reasoning and a concrete cleaning step for every issue found, making it a natural first pass before writing any cleaning script.