Prompt library

Proven prompts,in one place.

From research, official guides and the community. Each with its source.

  • 41prompts
  • 8categories
  • 37sources

41 prompts

01

Reasoning

Prompts that make the model think step by step instead of jumping to an answer.

9 prompts
02

Accuracy

Techniques that cut made-up answers and tie them to sources and checks.

5 prompts
  • Research★ PopularAdapted#

    Chain of verification

    The model checks its own answer: a draft, check questions, independent answers, a corrected final version.

    [Your question]
    
    Follow these four steps:
    1. Draft an initial response.
    2. Plan verification questions to fact-check the draft.
    3. Answer those questions independently, without looking at the draft.
    4. Generate the final verified response, fixing anything the answers contradict.
    FactScore on biographies · Llama 65B

    Precision on list questions doubled: 0.17 → 0.36.

  • Official guide★ Popular#

    Permission to say “I don’t know”

    The simplest guard against made-up answers: explicitly allow the model to admit uncertainty.

    [Task and document]
    
    If you’re unsure about any aspect or if the [document] lacks necessary information, say “I don’t have enough information to confidently assess this.”

    Anthropic: “This technique can drastically reduce false information.”

  • Official guideAdapted#

    Quotes first, then answer

    On long documents, grounds the answer in word-for-word quotes, so you can see where each claim comes from.

    <document>
    [Document]
    </document>
    
    Find quotes from the document that are relevant to [your question]. Place these in <quotes> tags. Then, based on these quotes, answer the question. Place your answer in <answer> tags.
  • Research#

    Answer “according to…”

    Steers the answer towards a trusted source; the model quotes more of what it really read.

    [Your question]
    Respond to this question using only information that can be attributed to [Wikipedia].

    Word-for-word quoting score (QUIP) on TriviaQA: 31.6 → 35.7 (ChatGPT).

  • Research#

    Clean the context first

    Strips the steering and opinion out of a question, so the model is less inclined to just agree with you.

    Given the following text by a user, extract the part that is unbiased and not their opinion, so that using that text alone would be good context for providing an unbiased answer to the question portion of the text. Please include the actual question or query that the user is asking. Separate this into two categories labeled with “Unbiased text context (includes all content except user’s bias):” and “Question/Query (does not include user bias/preference):”.
    
    Text by User: [original input]
    Factuality on opinionated questions · LLaMA-2-70B-chat

    Two steps: this prompt cleans the context; you then ask the question in a fresh chat, with the clean text only.

03

Better questions

Prompts that sharpen the question first, or have the AI ask you.

5 prompts
04

Writing

For clearer emails, copy and translations.

7 prompts
  • Research★ PopularAdapted#

    Show, don’t tell

    Instead of describing your style, give three examples; the model picks up tone, length and structure from them.

    Below are three [emails / posts] I wrote earlier. Study my tone, sentence length and structure; then write a new one about [topic] in the same style.
    
    Example 1: [...]
    Example 2: [...]
    Example 3: [...]
    TriviaQA accuracy · GPT-3 175B
  • Research#

    Give it a role

    Gives the model a point of view: the same text read through a lawyer’s, a marketer’s or a customer’s eyes.

    From now on, act as [persona]. Pay close attention to [what this persona cares about]. Provide outputs that [persona] would create.
  • PublicationAdapted#

    Bottom line up front

    An email technique from the military: the purpose and the action needed in the first line, details after.

    Rewrite the email below in BLUF (Bottom Line Up Front) form: a short opening statement that declares the purpose of the email and the action required, answering who, what, where, when and why. Then give the details as short bullet points.
    
    [Email]
  • Community★ Popular#

    English translator and improver

    Write in any language; it translates and hands back richer, more polished English.

    I want you to act as an English translator, spelling corrector and improver. I will speak to you in any language and you will detect the language, translate it and answer in the corrected and improved version of my text, in English. I want you to replace my simplified A0-level words and sentences with more beautiful and elegant, upper level English words and sentences. Keep the meaning same, but make them more literary. I want you to only reply the correction, the improvements and nothing else, do not write explanations. My first sentence is “istanbulu cok seviyom burada olmak cok guzel”

    From the most-starred prompt collection on GitHub; even the original sample sentence is Turkish. It was started by Turkish developer Fatih Kadir Akın.

  • Official guide#

    Grammar fixer

    Cleans up a text while keeping its meaning: spelling, punctuation, tense and word choice.

    Your task is to take the text provided and rewrite it into a clear, grammatically correct version while preserving the original meaning as closely as possible. Correct any spelling mistakes, punctuation errors, verb tense issues, word choice problems, and other grammatical mistakes.
    
    [Text]
  • Community★ PopularAdapted#

    Explain like I’m five

    Explains a complex topic without jargon, with an everyday comparison. Named after Reddit’s famous community.

    Explain [topic] to me like I’m five: use an everyday comparison, no technical terms, five sentences at most.
  • ResearchAdapted#

    Critique, then rewrite

    The model critiques and improves its own draft; lifts the quality of writing, code and plans in one go.

    [Task]
    
    First write a draft. Then critique the draft ruthlessly: list its weak points, gaps and mistakes. Finally, write an improved final version based on that critique.

    About 20% absolute improvement on average across 7 tasks (GPT-3.5, ChatGPT and GPT-4).

05

Business & strategy

Ready frames for decisions, risk, meetings and spreadsheets.

6 prompts
  • Publication★ PopularAdapted#

    Pre-mortem

    Assumes the project has already failed, so you see the risks without the optimism filter.

    Let’s run a pre-mortem. It is 12 months from now and [project / decision] has failed completely. Write down every plausible reason for its failure. Then pick the five most likely and most damaging, and suggest a step we can take today against each.
    
    Project: [short description]

    Per the 1989 study Klein cites, imagining that an event has already happened raises the ability to identify its causes correctly by 30%.

  • PublicationAdapted#

    Six thinking hats

    Looks at a decision from six separate angles in turn; stops one-sided discussions.

    Examine [decision] with Edward de Bono’s Six Thinking Hats. In order: White (information), Red (intuition and feelings), Black (caution), Yellow (benefits and values), Green (generating ideas), Blue (managing the thinking and concluding). Write 3–5 points per hat and end with a clear recommendation.
  • Research#

    What are the alternatives?

    Doesn’t settle for the first idea; compares the options with their pros and cons.

    Whenever I ask you to [task], if there are alternative ways to accomplish the same thing, list the best alternative approaches, compare/contrast the pros and cons of each with respect to [cost, time, risk], and include the original way that I asked. Then ask me which approach I would like to proceed with.
  • Official guide#

    Meeting scribe

    Turns meeting notes into a short summary of topics, decisions and action items with owners.

    Your task is to review the provided meeting notes and create a concise summary that captures the essential information, focusing on key takeaways and action items assigned to specific individuals or departments during the meeting.
    
    [Meeting notes]
  • Official guide#

    Long report to a short memo

    Distils a long report into one short memo for the team: trends, risks, takeaways.

    Summarize this annual report in a concise and clear manner, and identify key market trends and takeaways. Output your findings as a short memo I can send to my team.
    
    [Report]
  • Official guide#

    Excel formula expert

    Describe the calculation you need; get the advanced Excel formula and how it works.

    As an Excel Formula Expert, your task is to provide advanced Excel formulas that perform the complex calculations or data manipulations described by the user. If the user does not provide this information, ask the user to describe the desired outcome.
    
    [The calculation you need]

    Say which Excel version and language you use, so the function names match.

06

Learning

Turn the AI into a tutor, a sparring partner or an interviewer.

3 prompts
  • Research★ Popular#

    AI tutor

    Teaches by asking questions instead of handing over answers; used with students in Wharton classes.

    You are an upbeat, encouraging tutor who helps students understand concepts by explaining ideas and asking students questions. Start by introducing yourself to the student as their AI-Tutor who is happy to help them with any questions. Only ask one question at a time. First, ask them what they would like to learn about. Wait for the response.

    The full prompt is longer; this is its opening. The authors keep an updated version at moreusefulthings.com.

  • Official guide#

    Socratic sparring partner

    Asks probing questions instead of agreeing with you, so you see your own assumptions.

    You are an AI assistant capable of having in-depth Socratic style conversations on a wide range of topics. Your goal is to ask probing questions to help the user critically examine their beliefs and perspectives on the topic. Do not just give your own views, but engage in back-and-forth questioning to stimulate deeper thought and reflection.
    
    Topic: [topic]
  • Community★ Popular#

    Job interviewer

    Asks one question at a time and waits for your answer, like a real interview; the best rehearsal.

    I want you to act as an interviewer. I will be the candidate and you will ask me the interview questions for the [position] position. I want you to only reply as the interviewer. Do not write all the conversation at once. I want you to only do the interview with me. Ask me the questions and wait for my answers. Do not write explanations. Ask me the questions one by one like an interviewer does and wait for my answers. My first sentence is “Hi”
07

Code & agents

For assistants that write code and work on their own.

4 prompts
  • Official guide★ Popular#

    Three reminders for agents

    For the system prompt of an assistant that works on its own: keep going until done, don’t guess, plan.

    You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved.
    
    If you are not sure about file content or codebase structure pertaining to the user’s request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer.
    
    You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.

    OpenAI: these three instructions raised its internal SWE-bench Verified score by close to 20%; explicit planning alone added 4%.

  • Official guideAdapted#

    Explore, plan, code, commit

    Stops a coding assistant from jumping straight to code: read first, then plan.

    1. Read [the relevant files]; don’t write any code just yet.
    2. Make a plan for how to approach this problem and show it to me.
    3. Once the plan is approved, implement the solution in code.
    4. Commit the result and create a pull request.

    Anthropic: “Steps #1-#2 are crucial—without them, Claude tends to jump straight to coding a solution.” The live post has since changed; the original is archived.

  • Official guide#

    Go beyond the basics

    Newer models do exactly what is asked; if you want more, say so explicitly.

    Create [an analytics dashboard]. Include as many relevant features and interactions as possible. Go beyond the basics to create a fully-featured implementation.
  • Community★ Popular#

    Act as a Linux terminal

    The internet’s best-known “act as” prompt; for trying and learning the command line safely.

    I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is [pwd]
08

Magic phrases

Widely shared “booster” phrases, and what the evidence says today.

2 prompts

How we chose

Popular is not enough.

Models change fast: what a phrase did yesterday may differ today. Figures belong to the model and dataset in the source; for work that matters, try two versions and compare. Sources checked on November 6, 2026.

Let me teach these to your team.

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