10 AI Prompt Ideas That Unlock the Full Power of Any AI Tool

Most people use AI tools the same way every time — asking for summaries, drafting emails, generating basic content. The outputs are serviceable but rarely remarkable. What separates occasional AI users from people who genuinely transform their work with AI is not access to better tools. It is a fundamentally different approach to prompting: they bring specific AI prompt ideas that define not just the task but the role the AI should play, the constraints it should work within, and the format that will make the output immediately usable.

Below are 10 prompt ideas across 10 completely different use cases — creative writing, business strategy, learning, research, productivity, problem-solving, communication, personal development, content creation, and analysis. Each includes a fully worked example prompt, a breakdown of the prompting principles at work, and guidance on how to apply the same ideas to your own tasks.

A useful AI prompt idea is not just a topic or a task. It is an approach — a way of framing what you need that produces something more valuable than the obvious output. The best prompt ideas share three characteristics: they assign the AI a specific role rather than making a generic request, they specify the format and constraints that make the output immediately actionable, and they define what success looks like so the AI can work toward it rather than toward an average of what people usually want.

Claude is the AI tool best suited to the most demanding versions of these prompts because it reasons carefully, follows complex multi-part instructions, and acknowledges uncertainty rather than confabulating. Save the prompt frameworks that work for your specific tasks in Chat Smith as reusable templates.

Use case: critical thinking, debate preparation, strengthening arguments before presenting them, checking your reasoning.

I am going to share a position I hold. I want you to do two things: first, present the strongest possible case against my position — the steelman, not a strawman. Make the opposing argument as compelling as you can. Then, identify the two or three specific points in my own position that are weakest in light of that opposing case. Do not tell me what conclusion to draw. My position: [your position here].

Why this idea works: the explicit instruction to steelman rather than strawman is the critical distinction — most people unconsciously ask AI to confirm their views, and this prompt inverts that. Separating the two tasks (strongest counter-argument, then weaknesses in your position) produces a structured output you can act on. ‘Do not tell me what conclusion to draw’ keeps you in control of the decision while giving you the analytical material you need.

Apply this idea to: any belief, business decision, creative choice, or policy position you need to stress-test before acting on or presenting to others.

Use case: learning a new field, onboarding to a new role, building context before a meeting or interview.

I am transitioning from [your current field] into [new field]. I already understand [list 2-3 concepts from your current domain]. Explain [the new concept or field] to me by drawing explicit analogies to what I already know where possible. Then tell me: what are the 5 things that someone from my background most commonly misunderstands about this field? Keep the explanation under 500 words.

Why this idea works: telling the AI what you already know prevents it from explaining things you do not need explained and allows it to build on existing mental models rather than starting from zero. The misconceptions question is the most valuable part — the things people from your background most commonly misunderstand are the exact things you are most likely to misunderstand, and knowing them in advance is worth more than the general explanation.

Apply this idea to: learning any new technical domain, understanding a new industry before a sales call, preparing for a career transition, or building context before a high-stakes conversation.

Use case: project planning, risk assessment, strategy review, decision-making before committing to a major initiative.

I am about to [describe the project or initiative]. Imagine it is 12 months from now and it has failed completely. Do not describe a single dramatic failure — instead, identify the 5 most likely specific failure modes based on what commonly goes wrong in projects like this. For each one, rate the probability (low, medium, high) and suggest one concrete preventative action. Be direct and do not soften the analysis.

Why this idea works: the pre-mortem is a well-validated decision-making technique and this prompt applies it systematically. The instruction to identify multiple specific failure modes (not a single dramatic one) prevents the AI from producing an obvious disaster scenario and forces it toward the mundane, realistic failures that actually kill most projects. ‘Be direct and do not soften the analysis’ overrides AI’s tendency toward diplomatic hedging when the task calls for useful bluntness.

Apply this idea to: product launches, hiring decisions, business partnerships, major purchases, career moves, or any initiative where the cost of failure is high enough to justify planning around it.

Use case: creative projects, content creation, copywriting, design briefs, marketing ideation.

Generate 10 ideas for [creative project]. Apply the following constraints: the first 3 ideas should use a format or approach we have never tried before. The next 3 should be the simplest possible execution of the concept — one person, minimal resources, maximum impact. The final 4 should be deliberately provocative or counterintuitive. For each idea, add one sentence explaining why it might fail. Do not include any idea that is a variation on what we already do: [brief description of existing approach].

Why this idea works: unconstrained brainstorming produces lists of incremental variations on the obvious. Imposing different constraints on different segments of the list forces genuine diversity. The ‘why it might fail’ instruction prevents pure optimism and makes each idea more useful for decision-making. Telling the AI what you already do eliminates the entire category of suggestions you do not need.

Apply this idea to: any creative brief, marketing campaign, product feature list, content calendar, event concept, or problem that needs fresh solutions rather than more of the same.

Use case: learning, knowledge retention, identifying gaps in understanding, preparing to teach or present a topic.

I am going to explain [topic] to you as I currently understand it. After I finish, do three things: (1) identify any factual errors or significant misconceptions in my explanation, (2) identify the most important gap — the thing I clearly do not fully understand that is most relevant to the topic, and (3) give me the one question I should be able to answer confidently that would demonstrate I genuinely understand this subject. Here is my explanation: [your explanation].

Why this idea works: this is the Feynman technique applied through AI. The act of explaining something surfaces gaps in understanding that passive reading does not reveal. The three-part structure — errors, gaps, key question — produces targeted feedback that is immediately actionable. The ‘one question you should be able to answer’ is particularly valuable because it sets a concrete benchmark for understanding rather than leaving mastery undefined.

Apply this idea to: any subject you are studying, any skill you are developing, any concept you need to present to others, or any domain where you suspect your understanding is shallower than it should be.

Use case: strategic planning, policy analysis, product decisions, any choice where unintended consequences matter.

I am considering [decision or action]. Map out the likely consequences in three layers: immediate direct effects (what happens first), second-order effects (what happens as a result of the first effects), and third-order effects (what happens as the second-order effects play out over time). For each layer, separate the effects into those that benefit me and those that create problems. Focus on the non-obvious. Flag any second or third-order effect that could be significantly larger than the immediate effect.

Why this idea works: most analysis stops at first-order effects. The three-layer structure forces systematic second and third-order thinking that most people skip under time pressure. ‘Focus on the non-obvious’ pushes past the consequences the AI (and you) would have generated anyway. ‘Flag any second or third-order effect significantly larger than the immediate effect’ is the single most valuable instruction — these asymmetric downstream effects are where most strategic surprises actually come from.

Apply this idea to: pricing changes, hiring or firing decisions, product feature changes, communications strategy, partnership decisions, or any choice with significant downstream effects.

Use case: editing and improving written work, preparing for difficult conversations, stress-testing proposals before presenting them.

Read the following [document, email, proposal, argument] as the most sceptical, critical reader it will encounter — someone who is looking for reasons to reject it, dismiss it, or poke holes in it. List every specific objection this reader would make, in order from most damaging to least. Then identify the three objections I must address before sending this, because failing to address them would cost me credibility or the outcome I am seeking. [Paste document here.]

Why this idea works: writing is almost always reviewed in the mental model of a sympathetic reader, which produces work that performs well when the reader is already persuaded and poorly when they are sceptical. The hostile reader instruction corrects for this. Prioritising objections from most to least damaging produces an actionable editing list. The ‘three you must address’ instruction forces prioritisation rather than requiring you to respond to everything.

Apply this idea to: investor pitches, sales proposals, job applications, grant applications, internal strategy documents, important emails, or any written work where a sceptical audience can cost you something significant.

Use case: clarifying your own thinking, working through a complex decision, personal development, values exploration.

I want to think through [topic, decision, or belief] more carefully. Play the role of a Socratic interlocutor. Ask me one question at a time. Each question should push me to examine an assumption, clarify a vague term, or follow my own reasoning to its logical conclusion. Do not offer your own opinions or conclusions. Do not ask more than one question per turn. Continue until I have either arrived at a clearer position or identified where my thinking breaks down. Start with the first question now.

Why this idea works: most AI interactions involve asking for information or output. This prompt inverts the dynamic — the AI asks and you answer, which forces active thinking rather than passive consumption. The one-question-per-turn constraint prevents the AI from overwhelming you and keeps the dialogue genuinely Socratic rather than becoming a lecture with a question mark at the end. ‘Do not offer your own opinions’ preserves the process for producing your own conclusions rather than AI’s.

Apply this idea to: career decisions, ethical questions, business strategy, creative direction, personal values, or any domain where you need to think more carefully rather than just know more.

Use case: solving stuck problems, generating novel solutions, product design, innovation, overcoming creative blocks.

I am trying to solve this problem: [describe the problem]. Find 5 domains completely unrelated to mine that have solved a structurally similar problem. For each one, describe how they solved it and then explicitly translate that solution into a concrete approach I could apply to my problem. The domains should be genuinely diverse — at least two should come from nature or biology, and at least one from a domain I would not typically look to for business solutions.

Why this idea works: analogical transfer is one of the most powerful creativity and problem-solving techniques available, and it is one where AI has a genuine advantage over human experts — it has been trained across vastly more domains and can identify structural similarities that a domain expert would not. Requiring biological and nature analogies forces the AI into domains that have solved optimisation, resilience, and coordination problems over millions of years of evolution. The explicit translation step — ‘apply this to my problem’ — is what makes the insight actionable.

Apply this idea to: any problem where conventional approaches have failed, product design challenges, organisational design, pricing strategy, customer retention, growth challenges, or any situation where you need to think genuinely differently.

Use case: personal development, goal setting, career planning, motivation, clarifying what you actually want.

Write a one-page autobiography entry from the perspective of me in 10 years, looking back on the period between now and then. Here is what I currently want my life to look like in 10 years: [describe your goals and vision]. Write it in first person, past tense, as if the decade has already happened. Include what I did, what I overcame, what I gave up, what I am most proud of, and one thing I wish I had done differently. Make it honest rather than triumphant.

Why this idea works: prospective autobiographies written in the past tense have been shown to improve goal achievement because they make abstract futures feel concrete and lived. The specific elements requested — what you did, overcame, gave up, are proud of, wish you had done differently — force the AI to include the realistic cost and difficulty of achievement rather than a fantasy success narrative. ‘Honest rather than triumphant’ is the critical instruction that makes the output genuinely useful for planning rather than just motivating.

Apply this idea to: career planning, personal goal setting, relationship goals, creative ambitions, health and fitness planning, or any long-term vision that benefits from being made more concrete and emotionally real.

Looking across all 10 ideas, the same principles appear in different forms. Every strong prompt idea assigns a specific role or perspective to the AI rather than making a generic request. Every one specifies the format and structure of the output. Every one includes at least one instruction that overrides AI’s default behaviour — ‘do not soften the analysis’, ‘do not tell me what conclusion to draw’, ‘do not offer your own opinions’. And every one defines what success looks like so the AI can work toward a specific outcome rather than a generic response.

Save these prompt frameworks in Chat Smith as reusable one-click templates so the pre-mortem framework, the hostile reader review, and the Socratic dialogue are all immediately accessible when you need them. The value of a great prompt idea compounds when you have it ready to deploy rather than having to reconstruct it from memory each time.

The most common mistake is making a request rather than providing a brief. ‘Give me ideas for my business’ is a request. ‘Generate 10 growth ideas for a B2B SaaS company at £1M ARR, where at least 3 use channels we are not currently in and each includes a one-sentence rationale and a one-sentence risk’ is a brief. The second version produces something you can act on. The first produces something you have to sift through and evaluate before you can do anything with it.

The second most common mistake is not telling the AI what you do not want. AI models have strong defaults: they soften criticism, include caveats, offer balanced perspectives even when you want a decisive one, and produce outputs that look helpful rather than outputs that are useful. Overriding those defaults with specific negative instructions — ‘do not hedge’, ‘do not suggest I consult a professional’, ‘do not give me a balanced perspective’ — is one of the most reliable ways to get more useful outputs.

The difference between AI as a marginally useful tool and AI as something that genuinely changes how you work is almost entirely in the quality of the prompts you bring to it. These 10 AI prompt ideas are not techniques for specific tasks — they are frameworks for thinking about what you actually need from an AI interaction and how to ask for it in a way that produces something genuinely useful. Start with the one that addresses the task you are most stuck on right now. Apply it. Notice what changes. Then iterate.

The most valuable prompt ideas are the ones that are immediately accessible when you need them — not buried in a notes app or half-remembered from an article you read last month. Chat Smith lets you save your best prompt frameworks as one-click templates organised by use case, refine them with Claude until they produce exactly the output you need, compare the same prompt across multiple AI models to find which performs best for a given type of task, and share your most effective prompts with colleagues so your whole team benefits.

You can also use Claude to adapt any of these 10 prompt frameworks to your specific context — your industry, your role, your particular challenge — before using them. The framework is the idea. Claude helps you make it specific to your situation.