Browse AI prompts by task

Create an executable content production plan for [CONTENT IDEA] on [CHANNEL], aimed at [AUDIENCE]. Start by confirming the business or learning goal, available evidence, publishing constraints and desired complexity. Build a table with the main sections, supporting points, source requirements, format, owner and definition of done. Include two to four useful subtopics under each main section, but exclude angles that do not fit the channel or audience. Mark assumptions and research gaps instead of inventing facts. End with a realistic production sequence and review checklist.

Customize: [CONTENT IDEA], [CHANNEL], [AUDIENCE]

Create an interview practice plan for the role [JOB TITLE] using this job description: [JOB DESCRIPTION]. Identify the five most important competencies, explain what evidence a strong answer should contain, and write two practice questions per competency. Do not invent experience for the candidate. Add a scorecard that rewards specific evidence, clear reasoning and honest limits.

Customize: [JOB TITLE], [JOB DESCRIPTION]

Review this purchase-decision content for [OFFER]: [CONTENT]. Identify where it may reinforce only the audience's existing beliefs. Propose a balanced version that includes supporting evidence, credible limitations and reasonable alternatives. Separate verified facts from assumptions and add a checklist for source quality. Do not invent studies or selectively omit material counter-evidence.

Customize: [OFFER], [CONTENT]

Act as a classroom chemistry equation checker. I will provide a written reaction or a list of substances: [REACTION]. Explain whether a reaction is expected under the stated conditions, balance the equation when enough information is available, identify the reaction type, and state any uncertainty. This is a paper exercise only. Do not give instructions for physically mixing chemicals. Flag hazardous, unstable or unknown combinations and recommend checking a current safety data sheet and a qualified instructor.

Customize: [REACTION]

Identify the information gap between the seller and buyer for [OFFER] using [CUSTOMER QUESTIONS AND EVIDENCE]. Build an educational sequence that covers price, operation, limitations, alternatives, support and cancellation. Mark unanswered questions for investigation. Do not use education as a disguise for withholding material facts.

Customize: [OFFER], [CUSTOMER QUESTIONS AND EVIDENCE]

Help me compare these career options: [OPTIONS]. My actual experience, constraints and priorities are [BACKGROUND]. Build a decision matrix using criteria I can verify, such as required skills, training time, location, working conditions and realistic entry routes. Do not invent salary figures or guarantee an outcome. Mark facts that need current job-market research, then suggest one low-cost experiment for testing each option.

Customize: [OPTIONS], [BACKGROUND]

Create two YouTube ad openings for [PRODUCT OR SERVICE]: one situational comedy hook and one self-aware hook. Keep the audience, product claim and [CALL TO ACTION] identical. Add a plain control, brand-safety checklist and test hypothesis. Do not claim a hook will go viral or invent audience reactions.

Customize: [PRODUCT OR SERVICE], [CALL TO ACTION]

Compare these two historical sources about [EVENT OR PERIOD]: [SOURCE A] and [SOURCE B]. Identify who created each source, its date, intended audience, purpose and likely limitations. Separate what each source directly shows from later interpretation. Highlight agreements, contradictions and missing context. Do not invent details that are not present in the supplied sources.

Customize: [EVENT OR PERIOD], [SOURCE A], [SOURCE B]

Help me compare [POSITION A] and [POSITION B] on [QUESTION]. Define each position in plain language, state the strongest argument for each, identify one common misunderstanding, and show where their assumptions differ. Use sources I provide and label any historical claim that needs verification. Finish with three questions that test whether I can explain the disagreement in my own words.

Customize: [POSITION A], [POSITION B], [QUESTION]

Create a decision-support campaign for [OFFER] and [AUDIENCE] using these options and criteria: [DETAILS]. Provide a short chooser, a comparison matrix and a route to human help. Keep meaningful alternatives visible and explain when the offer is not a fit. Return the flow and measurement plan. Do not use forced-choice or deceptive defaults.

Customize: [OFFER], [AUDIENCE], [DETAILS]

Create a defensive threat model for [SYSTEM]. The architecture, users, data and trust boundaries are [DETAILS]. Identify assets, entry points, likely abuse cases and existing controls. Rank risks by likelihood and impact without inventing vulnerabilities. Recommend preventive and detective controls, owners and verification steps. Do not provide exploitation instructions. Ask for missing architecture details before making high-confidence claims.

Customize: [SYSTEM], [DETAILS]

Create a campaign for [OFFER] and [AUDIENCE] that reflects a shared community value supported by [RESEARCH]. Separate observed language from assumptions, avoid stereotypes and do not imply that purchase determines group membership. Provide a community-value variant, a neutral control, the hypothesis and guardrail checks for exclusion or misrepresentation.

Customize: [OFFER], [AUDIENCE], [RESEARCH]

Write a surreal first-person monologue about [SITUATION] for [FORMAT]. Build a consistent internal logic from three unusual details I provide: [DETAILS]. Keep the piece fictional, avoid mental-health labels as insults, and do not imitate a living writer. Use concrete imagery and return a short note explaining how the recurring motifs connect.

Customize: [SITUATION], [FORMAT], [DETAILS]

Create an SVG icon for [CONCEPT] at [SIZE]. Use a simple viewBox, scalable paths, currentColor where appropriate and no external resources. If the icon conveys meaning, include a concise title and explain the accessible name needed in HTML. If it is decorative, mark it for aria-hidden use. Return the SVG code, a short usage example and checks for contrast, focus and scaling.

Customize: [CONCEPT], [SIZE]

Create a fill-in-the-blank worksheet for a [LEVEL] learner practising [TOPIC]. Use this source text or vocabulary list: [SOURCE MATERIAL]. Produce [NUMBER] items that progress from recognition to application. Include an answer key and one-sentence explanation for each answer. Avoid clues that make the answer obvious. After the worksheet, suggest how to adjust the next set if the learner scores below 60%, between 60% and 85%, or above 85%.

Customize: [LEVEL], [TOPIC], [SOURCE MATERIAL], [NUMBER]

Design a campaign test for [OFFER] and [AUDIENCE] using affective forecasting carefully. Describe a realistic future experience supported by [EVIDENCE], then create a neutral control and one future-outcome variant. Do not exaggerate emotions, invent results or promise an outcome. Define the hypothesis, primary metric, guardrail metric and decision rule.

Customize: [OFFER], [AUDIENCE], [EVIDENCE]

Create an open-loop campaign for [OFFER] aimed at [AUDIENCE]. Use curiosity to sequence verified information, but reveal the essential terms, price and limitations before asking for action. Provide the opening, follow-up and resolution message. Do not use deceptive cliffhangers or hide material facts. Add a checklist for checking clarity and informed choice.

Customize: [OFFER], [AUDIENCE]

Draft a landing-page outline for [BRAND] that demonstrates expertise in [FIELD] using only [CREDENTIALS, WORK AND EVIDENCE]. Include the audience problem, proof blocks, limitations and a clear next step. Separate qualifications from opinions and mark unsupported claims for removal. Do not invent awards, clients or credentials.

Customize: [BRAND], [FIELD], [CREDENTIALS, WORK AND EVIDENCE]

Create four image-generation prompt variants for [SUBJECT] and [PURPOSE]. Keep the core subject consistent while varying composition, viewpoint, lighting and visual medium. Include negative constraints only when they solve a stated problem. Do not name living artists or claim a particular tool feature unless I specify the tool and version. Return each variant with a short note explaining what changed.

Customize: [SUBJECT], [PURPOSE]

Create a time-sensitive campaign for [OFFER] using only this genuine deadline or event evidence: [EVIDENCE]. Explain the opportunity, deadline, eligibility and what happens afterward. Provide a factual control and an urgency variant with identical material terms. Avoid social exclusion, anxiety cues, fake activity notices and invented scarcity.

Customize: [OFFER], [EVIDENCE]

Debug this R code: [CODE]. The R version, package versions, input data shape and exact error are [ENVIRONMENT]. First reduce the problem to a minimal reproducible example. Explain the likely cause, propose the smallest correction, and provide commands for verifying the result. Do not invent package behavior or pretend to run the code. Mark any conclusion that depends on missing data.

Customize: [CODE], [ENVIRONMENT]

Define the minimum viable user flow for [PRODUCT IDEA] and [TARGET USER]. Start with the user problem, triggering event and successful outcome. Separate essential steps from later enhancements, map the smallest testable flow and identify the riskiest assumption. Return the MVP boundary, excluded features, acceptance criteria and a one-week validation plan. Do not invent market demand or user evidence.

Customize: [PRODUCT IDEA], [TARGET USER]

Help define ten organizational principles for [ORGANIZATION] using [COMPANY EVIDENCE] and [LEADERSHIP VALUES]. For each principle, provide a plain-language name, observable daily behaviour and one counterexample. Flag conflicts or unsupported values before finalizing the set. Return a review table and ask which principles need revision. Do not infer culture from branding language alone.

Customize: [ORGANIZATION], [COMPANY EVIDENCE], [LEADERSHIP VALUES]

I want you to act as a Machine Learning Engineer. I will provide you with a dataset of customer behavior on an e-commerce website, and I want you to develop a recommendation system using collaborative filtering to suggest products to customers based on their purchase history. My first request is "Develop a recommendation system using collaborative filtering to suggest products to customers on an e-commerce website based on their purchase history."