Prompts

The working prompts, free to copy.

130 prompts pulled directly from our AI Practice and Situations guides. The prompts themselves are free to copy, the documented frameworks they upgrade are what we sell.

Learning a Hard Skill as an Adult

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The mastery-level test
"Here's a concept or sub-skill I think I've learned: [describe it]. Design a genuinely novel test scenario, one I haven't seen before, that would reveal whether I can actually apply it, not just recognize or recall it."

Why it works: a novel test case exposes the gap between recognition and real application that familiar review material tends to hide.

The deliberate practice drill
"Here's the specific weakness I've identified: [describe it]. Design a focused practice drill that isolates just this weak point, with a way to get fast feedback on whether each attempt improved or not."

Why it works: isolating the weak point and building in fast feedback is what separates deliberate practice from the naive repetition that produces hours of effort without proportional improvement.

Deciding on a Real Career Change

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The feedback-analysis pattern
"Here's a list of past roles and projects, and what I expected versus what actually happened in each: [describe them]. Help me identify the pattern, where I consistently exceeded expectations, that points to my actual demonstrated strengths."

Why it works: comparing expectation to actual outcome across multiple instances surfaces a pattern that a single moment of reflection on "what am I good at" tends to miss.

The meaning specificity check
"Here's what felt meaningful to me in past work, described as specifically as I can: [describe it]. Help me check honestly whether the role I'm considering could plausibly contain more of that specific thing, not meaning in general."

Why it works: forcing specificity catches the common mistake of chasing a vague notion of "more meaningful work" toward a role that doesn't actually contain the particular thing that mattered.

The rethinking pass
"Here's my current lean on this career decision and my reasoning: [describe it]. Argue the strongest case for the opposite conclusion, and tell me what evidence would actually have to be true for that case to win."

Why it works: genuinely arguing the opposite case surfaces weaknesses in the original reasoning that simply restating your position tends to reinforce rather than test.

Having the Conversation You Keep Avoiding

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The opening-question draft
"Here's the dreaded conversation I need to have: [describe it]. Help me draft an opening calibrated question, a "how" or "what" question, that opens this topic without stating my conclusion first or triggering an immediate defensive reaction."

Why it works: a question-first opening keeps the other person in a problem-solving posture rather than a defensive one from the very first line.

The worth-versus-behavior check
"Here's what I'm planning to say in this hard conversation: [paste it]. Check whether any part of it reads as a judgment of who the person fundamentally is, rather than a specific behavior or decision, and help me rewrite it if so."

Why it works: identity-level language is often unconscious, and a dedicated check catches it before it derails the conversation into a defense of character.

The reaction map
"Here's the conversation I'm about to have and here's how I expect the other person might react: [describe both]. For each likely reaction, help me prepare one calibrated question or response that keeps the conversation moving rather than escalating or stalling."

Why it works: preparing for the other side's likely reactions, not just your own opening line, is what actually determines whether the conversation survives the first pushback.

Staying Focused When Everything Feels Urgent

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The urgent-importance sort
"Here's everything currently competing for my attention: [list them]. Sort each item into Covey's four quadrants, urgent/important, urgent/not important, not urgent/important, not urgent/not important, and tell me which quadrant is getting too much of my time right now."

Why it works: an explicit sort against a known structure catches items that feel urgent but don't actually belong in the top-priority quadrant, which is harder to see from inside the overwhelm.

The protected block design
"Here's my current week's schedule and the most important work I keep not getting to: [describe both]. Help me find one realistic, protectable block of time for it, and list the specific interruptions, notifications, people, recurring requests, that would need to be blocked for it to actually hold."

Why it works: naming the specific interruptions in advance turns a vague intention to focus into an enforceable boundary.

The recurring-urgency trace
"This specific kind of "urgent" request keeps coming up: [describe the pattern]. Help me trace it upstream, is this a bad handoff, unclear ownership, or a process that defaults to escalation, so I can look at fixing the source instead of just absorbing it each time."

Why it works: naming the likely systemic cause turns a recurring frustration into something concrete enough to actually fix, rather than something to keep managing reactively.

Recovering a Project That's Gone Badly Wrong

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The honest-status round
"Here's a project that may be in trouble: [describe it]. Draft a specific set of status questions I could ask each contributor that are hard to answer optimistically, questions that require a concrete number or fact rather than a general impression."

Why it works: specific, fact-based questions are harder to answer with comfortable vagueness than an open "how's it going," which tends to invite an optimistic summary by default.

The next-right-action focus
"Here's the real, honest state of a project in trouble: [describe it]. Help me identify the single most important next action given this reality, separate from any discussion of how we got here."

Why it works: isolating the next action from the history of how things went wrong keeps the immediate response focused on what's actually changeable right now.

The blameless post-mortem structure
"A project just recovered from a serious setback: [describe it]. Help me draft a post-mortem structure that asks what information was available at each key decision point, rather than who made which call, so the review stays focused on the process rather than on individuals."

Why it works: structuring the questions around information and process, rather than people, makes it easier for participants to be honest about what actually happened.

Keeping Your Best People From Leaving

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The disengagement signal check
"Here's a specific person on my team and what I've noticed recently: [describe changes in behavior, participation, or energy]. Help me assess whether this looks like early disengagement worth a direct conversation, and suggest how I might open that conversation."

Why it works: naming specific behavioral changes, rather than a vague sense that something's off, makes it easier to decide whether a conversation is warranted and how to start it without sounding alarmed.

The honest performance conversation
"I need to have an honest conversation with a strong performer about [describe the situation, business reality, or performance gap]. Help me draft an opening that's direct and specific without either softening the truth or sounding harsh."

Why it works: a drafted, specific opening is easier to deliver well than an improvised one, especially when the instinct under pressure is to soften a message that needs to stay clear.

The retention risk review
"Here's what I know about this person's engagement, time in role, and recent feedback: [describe the data you have]. Help me assess whether this pattern suggests real flight risk, separate from my general gut feeling about how they seem day to day."

Why it works: checking the actual data against gut feel catches cases where a quiet, undemonstrative person is at real risk despite seeming fine on the surface.

Staying Calm When Everything Is Going Wrong

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The worst-case rehearsal script
"Here's a high-stakes situation I can see coming: [describe it]. Help me identify the specific worst-case version of it, and draft exactly what I would do, step by step, if it happened."

Why it works: a concrete, rehearsed procedure gives you something specific to execute in the moment, rather than needing to improvise a calm response from nothing.

The facts-first check
"Here's a developing situation and what I currently believe is happening: [describe it]. Help me separate what I actually know to be true from what I'm assuming or fearing, before I decide what to do about it."

Why it works: explicitly separating confirmed fact from fear-driven assumption catches a reactive decision being made on an inaccurate picture.

The control sort
"Here's everything about this crisis that's currently on my mind: [list it]. Sort it into what I can still influence and what's already outside my control, and help me identify where my attention is currently being spent on the second list."

Why it works: an explicit sort makes visible exactly how much mental energy is going toward something that can no longer be changed, which is often more than it feels like from inside the stress.

Winning Clients Without Free Pitching

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The paid-diagnostic offer
"A prospect is asking for a free strategic proposal before committing: [describe the situation]. Help me design a smaller, paid diagnostic engagement I could offer instead, one that provides real value and demonstrates my approach without handing over the full strategic work for free."

Why it works: a concrete paid alternative resolves the prospect's reasonable desire for evidence without requiring the free work that undermines positioning.

The narrow-positioning test
"Here's my current range of services and client types: [describe them]. Help me identify the narrowest, most specific version of my expertise that still has enough demand to sustain the business, and show me what I'd be turning away."

Why it works: seeing the turned-away segment explicitly makes the tradeoff concrete, rather than an abstract fear of narrowing too far.

The self-orientation check
"Here's a pitch or proposal I'm about to send: [paste it]. Check whether it reads as primarily serving my interest in winning the business, or the prospect's interest in solving their actual problem, and suggest changes that lower the self-orientation."

Why it works: most pitches are written from the seller's perspective by default, and a dedicated check catches language that signals self-interest more than genuine fit assessment.

Bringing AI Into a Creative Project Without Ruining It

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The reaction check
"Here's a rough idea I'm working on: [describe it]. Don't suggest changes yet. Just tell me what you think is actually working in it, specifically."

Why it works: asking for what's working first, before critique, surfaces what's distinctive about your specific version rather than immediately pulling it toward a more generic, safer shape.

The stripping-back test
"For this piece: [describe it], if I removed [specific element], would it genuinely get worse, or would it just get different? Be honest, not diplomatic."

Why it works: this tests a specific element against removal rather than against taste, which catches load-bearing choices you might otherwise cut simply because they currently feel unfashionable or overly bold.

The constraint generator
"Give me five different, specific constraints I could apply to this project, not ideas, actual rules or limitations that would shape what I make."

Why it works: a specific constraint reliably produces more distinctive work than an open field of options, and generating several lets you pick the one that actually creates useful friction for this project.

The unstuck redirect
"I'm stuck at exactly this point: [describe it]. Give me three genuinely different directions I could take from here, not one polished answer."

Why it works: multiple divergent directions break a stall better than one confident-sounding suggestion, because the point isn't to accept the first idea, it's to see the shape of the actual decision you're facing.

AI for Hiring Decisions: Where Claude and ChatGPT Actually Help

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The rubric builder
"I'm hiring for [role]. Build me a structured interview rubric for [specific competency], with three questions and a description of what a strong, adequate, and weak answer actually sounds like for each."

Why it works: a rubric written before the interview, not scored impressionistically afterward, is the single most-replicated fix for inconsistent hiring judgment in the structured-interview research.

The evidence audit
"Here are my interview notes on two candidates for the same role: [notes]. For each of my stated conclusions about them, tell me whether it's backed by a specific example in the notes, or whether it's an impression without a cited example."

Why it works: this catches the gap between "she seemed really sharp" (impression, no example) and "she caught an edge case in the take-home the other two candidates missed" (evidence), which is exactly the distinction unstructured hiring judgment tends to blur.

The disconfirming-evidence check
"For the candidate I'm currently leaning toward, list every piece of evidence in these notes that argues against hiring them, even minor ones. Don't soften it."

Why it works: once a preference forms, it's easy to unconsciously discount contrary evidence; explicitly asking for the case against your current favorite forces you to weigh it rather than skip past it.

The reference-question generator
"Generate five reference-check questions specific to [role and concern, e.g. 'whether they can operate with minimal oversight'], each designed to get a concrete example rather than a generic endorsement."

Why it works: most reference checks default to "would you recommend them," which produces almost uniformly positive, low-signal answers; questions built around a specific concern and demanding an example produce far more usable information.

Using AI to Prep for a Negotiation: What Actually Works

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The interest map
"I'm negotiating [situation]. Here's what I want and why, and here's what I know about what they want and why: [details]. List the interests underneath both of our stated positions, not just the positions themselves, and flag any interest of theirs I might be missing."

Why it works: negotiation theory since Fisher and Ury has drawn a hard line between positions (what someone says they want) and interests (why they want it), and most unprepared negotiators only ever map the position. Forcing the interest layer surfaces trades neither side would find by arguing the stated numbers.

The leverage audit
"Given [situation], list my actual leverage, not what I wish I had, and list theirs. Then tell me honestly whether my walk-away position is credible or if I'm bluffing."

Why it works: an AI has no ego investment in your leverage being stronger than it is, which makes it a genuinely useful check against the self-flattering version of your position you'd otherwise walk in with.

The objection pre-mortem
"Before I go into this negotiation, list the five most likely objections or pushback points, ranked by how likely each is, and a one-line response to each that doesn't sound defensive."

Why it works: most people freeze in a negotiation not because the objection is unanswerable, but because they're hearing it for the first time in the room. Pre-mortem prep converts a live surprise into a rehearsed beat.

The roleplay
"Play the other party in this negotiation. Take a realistic hard-but-fair stance based on [their likely position]. I'll make my case, and you respond as they plausibly would, then tell me afterward where my argument was weak."

Why it works: rehearsal under mild pressure is a documented way to reduce in-the-moment freezing, and an AI is a patient, always-available sparring partner for it, even if its impression of the other party is necessarily a guess.

AI Won't Write Your Best Work, But It Can Improve It

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The specificity check
"Read this paragraph: [paste it]. Flag every sentence that sounds confident but doesn't actually commit to a specific, checkable claim."

Why it works: this catches the exact failure mode of prose that reads fine but says nothing, sentences built to sound finished rather than to mean something particular.

The filler cut
"List every phrase in this paragraph that could be deleted without losing any actual information, not just words that sound informal, but genuine padding."

Why it works: most drafts carry more hedging and throat-clearing than the writer notices on a first read, and naming it explicitly makes it easy to cut in one pass.

The claim audit
"For each claim in this paragraph, is there a specific detail, example, or number backing it up, or is it asserted without support? List the unsupported ones."

Why it works: an unsupported claim often reads as fine in isolation and only reveals itself as empty once directly tested against whether anything specific actually backs it.

The read-aloud test
"Read this paragraph back to me exactly as written, and flag any sentence that would sound unnatural spoken out loud."

Why it works: writing that sounds fine silently often reveals itself as stilted the moment it's voiced, catching a specific artifact most silent read-throughs miss entirely.

Can AI Actually Help You Make Better Decisions?

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The base rate check
"Before I decide on [situation], tell me: across similar decisions in general, what typically happens? I want the base rate, not my specific case, before we talk about my specific case."

Why it works: most people jump straight to reasoning about their specific situation and skip the outside comparison entirely, which is exactly the step Kahneman's research identifies as most commonly missing from ordinary judgment.

The premortem
"Imagine it's a year from now and this decision, [decision], turned out to be a mistake. Write the explanation of what went wrong, in detail, as if it already happened."

Why it works: framing failure as having already happened, rather than as a hypothetical risk, makes people (and AI, reasoning the same way on your behalf) far more specific about actual failure modes than a generic "what could go wrong" question does.

The outside view
"If a stranger with no emotional stake in this decision looked at exactly what I've told you, what would they say I'm not weighing correctly?"

Why it works: removing the emotional framing surfaces the parts of the decision you've been quietly discounting because you're inside it, a documented gap between how we judge our own situations and how we judge someone else's identical one.

The reversibility test
"Is this decision actually reversible if it goes badly, or does it look reversible but isn't? Be specific about what reversing it would actually require."

Why it works: a huge share of decision anxiety comes from treating reversible choices like irreversible ones (and vice versa), and AI is good at helping map out the actual, specific cost of reversing course once you ask it directly.

The AI Prompts That Make a Feedback Conversation Easier to Prepare

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The clarity check
"Read this feedback draft: [paste it]. If you were the recipient, what specifically would you think I'm asking you to change? If it's unclear, say so rather than guessing generously."

Why it works: this catches feedback that's been softened into ambiguity, where the AI, standing in for the recipient, genuinely can't identify the actionable point.

The care-and-challenge audit
"Score this draft on two separate axes: does it show I care about this person specifically, and does it clearly challenge the behavior that needs to change? Where is it weak on either axis?"

Why it works: scoring the two axes independently catches the specific failure mode of thinking you're being kind (high on care, apparently) while actually just avoiding the challenge entirely, or vice versa.

The roleplay rehearsal
"Play [describe the person, defensive, likely to get emotional, likely to argue back] hearing this feedback for the first time. React realistically, then tell me afterward what part of my delivery would have landed worst."

Why it works: rehearsing against a plausible reaction, while the stakes are still zero, surfaces where your own delivery would land badly before you find out in the real, much higher-stakes conversation.

The reversal check
"Here's feedback I received recently: [paste it]. Using the same care-and-challenge framework, was this actually caring, actually direct, both, or neither?"

Why it works: running the framework on feedback you received, not just feedback you're giving, helps you separate a legitimately hard truth you're resisting from an actually unfair or poorly delivered message, a distinction that's much harder to make in the moment than in hindsight.

Learning Faster With AI: A Realistic Look

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The retrieval quiz
"Quiz me on [topic] with five questions, one at a time, don't show me the next question until I've answered the current one, and tell me if I'm wrong before moving on."

Why it works: this forces retrieval, producing the answer from memory rather than recognizing it in a text, which is documented to build far stronger retention than re-reading the material again.

The interleaved practice set
"Give me ten practice problems on [topic], but mix in problems from [a related topic I've already covered] randomly, don't group them by type."

Why it works: mixing problem types (interleaving) forces you to first identify which method applies before applying it, which is closer to how you'll actually need to use the skill later than practicing one method in a block, where the method is already given away by the fact that every problem in the block uses it.

The explain-it-back check
"I'm going to explain [concept] back to you in my own words. Tell me specifically what I got wrong or oversimplified, don't just tell me if I'm generally right."

Why it works: producing an explanation yourself is a stronger test of real understanding than recognizing a correct one, and asking for specific errors rather than a general verdict catches the parts you've quietly glossed over without noticing you'd done it.

The spaced review generator
"Generate three new questions on [topic I learned a week ago], different from any I've seen before, to check what's actually stuck."

Why it works: spacing practice out over time, rather than massing it into one session, is one of the more robustly replicated findings in learning research, and fresh questions each time prevent you from just memorizing the specific question rather than the underlying concept.

AI and the Hard Part of Managing Clients

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The trust-input audit
"Here's what's changed in this client relationship recently: [describe it]. Score, roughly, whether the issue looks like a credibility problem, a reliability problem, or an intimacy problem, credibility meaning expertise and track record, reliability meaning consistency and follow-through, intimacy meaning psychological safety and candor."

Why it works: forcing a choice between three distinct categories produces a sharper diagnosis than an open-ended "what's wrong," because each category points to a different fix.

The self-orientation check
"Be honest: in my recent interactions with this client, is there anywhere I was more focused on looking competent or protecting my position than on their actual problem?"

Why it works: this is the input most people skip entirely, since it requires looking at your own behavior rather than the client's, and it's exactly the input the documented equation treats as capable of undermining all the others.

The evidence sort
"List every specific, dated example I've given you of this relationship changing. For each, is it clearly about me, clearly about their internal situation, or genuinely ambiguous?"

Why it works: it's easy to attribute every signal to your own performance under stress, and separating genuinely ambiguous signals from clear ones keeps you from over-correcting on a read that isn't actually about you.

The next-conversation prep
"Based on this read, draft three questions I could ask the client directly that would surface which trust input is actually weakest, without sounding defensive or needy."

Why it works: a diagnosis that never becomes an actual conversation with the client doesn't fix anything; this converts the internal read into something you can actually say out loud.

Prepping for a Meeting With Claude, ChatGPT, or Gemini

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The output definition
"I have a meeting about [topic]. Before anything else, tell me: what specific decision, document, or resolved ambiguity should exist by the end of it that doesn't exist right now?"

Why it works: naming a concrete output forces a decision about what the meeting is actually for, rather than letting it default to a discussion that can end anywhere.

The attendee-intent map
"Here's who's attending and their roles: [list them]. For each, what do they likely want out of this meeting, and where might that conflict with the stated output?"

Why it works: surfacing likely conflicts before the meeting starts means you're not discovering them live, in front of everyone, for the first time.

The objection pre-empt
"What's the strongest objection someone in this meeting could raise against the outcome I'm hoping for, and how would I actually respond to it?"

Why it works: rehearsing the hardest pushback in advance means you're not improvising a response to it for the first time under pressure, in the room.

The email-instead check
"Given this meeting's intended output, could it actually be achieved with a written message and a reply instead of a live meeting? Be honest."

Why it works: this is the check most meetings skip entirely, and an honest answer sometimes reclaims an hour of everyone's calendar that a live meeting was never actually required for.

Pricing Your Work With AI: What ChatGPT and Claude Can (and Can't) Tell You

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The value translation
"I'm pricing [project] for a client. Here's what the work actually produces for them: [describe the outcome, revenue impact, time saved, risk avoided]. Translate that into a dollar estimate of value to them, not a price for my time."

Why it works: most sellers can describe what they do but struggle to translate it into the client's own financial terms; forcing the translation surfaces a number that's usually far higher than an hourly calculation would produce.

The range generator
"Given a value estimate of roughly [$X] to the client, and market rates for comparable work in [industry], suggest three pricing tiers, a conservative, a target, and an ambitious number, with the reasoning for each."

Why it works: a single number invites a simple yes-or-no; three tiers turn the conversation into a choice, and the anchoring effect of the ambitious tier tends to make the target tier feel more reasonable by comparison.

The objection pre-mortem
"List the five most likely objections to this price, ranked by how likely each is, and a response to each that doesn't immediately discount."

Why it works: most pricing conversations fail not because the price was wrong but because the seller wasn't ready for the specific pushback and reflexively discounted under pressure; rehearsing the objection in advance keeps the number intact.

The walk-away check
"If the client pushed back hard and asked for 30% off, what would I actually lose by holding the price, and what would I lose by discounting? Be specific."

Why it works: this forces an honest look at whether your price is actually defensible or whether you'd cave under mild pressure, which tells you more about your own confidence in the number than any market-rate lookup does.

How Product Teams Are Actually Using AI to Make Decisions

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The outcome check
"For each of these roadmap items: [list], state the specific user or business outcome it's meant to serve. If you can't state one clearly from what I've given you, flag it rather than guessing."

Why it works: this surfaces roadmap items that were never actually tied to a real outcome in the first place, dressed up as obviously worth doing.

The assumption audit
"For [specific feature], list the assumptions we're making that haven't been validated yet, and rank them by how damaging it would be if each turned out false."

Why it works: most risky roadmap bets fail on one specific unvalidated assumption, not on execution, and naming them explicitly turns a vague worry into a specific, checkable risk.

The kill-criteria test
"Before we build this, what evidence, if we saw it in the first two weeks after shipping, would tell us this was the wrong call? Be specific, not just 'low engagement.'"

Why it works: defining failure in advance, concretely, makes it far easier to actually kill a bad bet later, instead of quietly redefining success after the fact to match whatever happened, which is the usual, quieter way a bad bet survives past the point it should have been cut.

The stakeholder-lens read
"Read this roadmap decision as a skeptical [engineering lead / sales lead / support lead]. What's the strongest objection they'd raise, and is it about the decision itself or about how it was communicated?"

Why it works: separating a substantive objection from a communication problem prevents good decisions from getting killed by bad rollout, and bad decisions from surviving because they were pitched well.

Getting Ready for a Discovery Call With Claude or ChatGPT

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The situation map
"I have a discovery call with [role] at a [industry] company. Generate five open situation questions to understand their current setup before I pitch anything."

Why it works: situation questions establish the baseline SPIN's research treats as foundational, and generating them before the call keeps the conversation from opening on your product instead of their context.

The implication ladder
"Given this likely problem: [describe it]. Write three implication questions that help the prospect surface the cost of not solving it, without me stating the cost for them."

Why it works: Rackham's research specifically found that implication questions, ones that get the prospect to articulate consequences themselves, correlated with success far more than a rep asserting the cost directly.

The objection library
"List the five most likely objections a [role] at a company this size would raise about [category of solution], ranked by likelihood, with a question-based response to each rather than a rebuttal."

Why it works: a question-based response keeps the prospect talking and surfacing more information, rather than shutting the objection down with a defensive answer that ends the thread.

The role-play rehearsal
"Play a skeptical [role] hearing my pitch for the first time. Push back realistically, and afterward tell me which of my questions actually got you talking versus which ones I could have skipped."

Why it works: rehearsing under mild simulated pushback surfaces which of your planned questions are actually doing work and which are filler, before you find out live on the call, when the cost of finding out is a real prospect losing patience and moving on to a competitor who asked better questions first.

Stress-Testing a Strategy With AI: A Method for Claude and ChatGPT

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The diagnosis check
"Read this strategy document: [paste it]. Find the specific sentence or section that states the actual problem or obstacle this strategy is solving for. If you can't find one, say so explicitly rather than inferring one."

Why it works: this directly tests for the single most common failure in real strategy documents, missing diagnosis, rather than asking a vague "is this good" question that a generic review would answer with praise.

The coherence audit
"List every initiative or action item in this document. For each one, does it follow logically from the stated goal, or does it look like it was added independently without connecting back to the diagnosis?"

Why it works: strategy documents often accumulate initiatives added for unrelated reasons (a stakeholder's pet project, last year's leftover plan), and this surfaces which actions are actually load-bearing versus decorative.

The competitor's-eye read
"Read this plan as if you were a smart competitor who wants to beat it. What's the most effective response you could make, and what part of this plan does it exploit?"

Why it works: strategy documents are usually written entirely from the inside, and forcing an adversarial outside perspective surfaces vulnerabilities the authors were structurally unlikely to notice themselves.

The resource-reality test
"Given the budget and headcount described in this document, is the stated ambition actually achievable, or is there a mismatch between what's promised and what's resourced?"

Why it works: an unrealistic resource-to-ambition ratio is one of the most common and most avoidable strategy failures, and it's a purely arithmetic check an AI can run reliably once given the actual numbers.

Thinking Like an Investor With AI: What Gets Right and What It Doesn't

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The circle-of-competence check
"I'm considering [investment]. Ask me to explain, without notes, what this business will likely look like in ten years and why. Then tell me honestly whether my answer showed real understanding or just familiarity with the name."

Why it works: this directly tests Buffett's own documented standard, real understanding versus surface familiarity, rather than letting confidence about a well-known name substitute for actual comprehension.

The margin-of-safety test
"Here's my estimate of this investment's value and the price being asked: [numbers]. If my estimate is off by 30% in the wrong direction, am I still roughly okay, or badly exposed?"

Why it works: this forces an explicit check on whether the price paid leaves real room for being wrong, rather than only for being right, which is the actual discipline behind margin of safety.

The base-rate pull
"For investments with these general characteristics: [describe the category, not the specific stock], what does the historical record show about how often this type of bet has worked out?"

Why it works: asking about the category instead of the specific pick avoids inviting a hallucinated-sounding opinion about one company, and grounds the conversation in a broader pattern instead.

The inversion pass
"Instead of telling me why this investment succeeds, list the specific ways it could fail, ranked by how likely each one is."

Why it works: inversion surfaces failure modes an optimism-shaped research process tends to skip past, and it's a genuinely different, harder question than the reassuring one most people default to asking.

Using AI as a Consultant or Fractional Executive

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The scope-drift check
"Here's the original engagement scope: [paste it]. Here's what I've actually been asked to do over the past month: [describe it]. Where has this drifted from the original scope, and by how much?"

Why it works: scope drift happens gradually enough that no single request feels like the moment it happened, and comparing the current pattern against the original document in one pass surfaces the accumulated gap that no individual week would have shown.

The value translation
"This engagement is producing [describe the outcome, revenue impact, risk avoided, time saved] for the client. Translate that into a dollar estimate of value, not a rate for my time, and tell me what fee that would justify."

Why it works: most consultants can describe what they did but struggle to translate it into the client's own financial terms, and forcing the translation usually produces a number well above an hourly calculation.

The trust-input audit
"Here's what's changed in this client relationship recently: [describe it]. Score whether the issue looks like a credibility problem, a reliability problem, or an intimacy problem, and whether I might be contributing through my own self-orientation."

Why it works: separating the four inputs forces a specific diagnosis instead of a vague sense that the relationship feels different, and the self-orientation check catches the one input most people are least willing to examine in themselves.

Using AI as a First-Time Founder

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The hiring-timing check
"We're considering hiring for [role] at [cost]. Current monthly revenue is [amount] and runway is [months]. Given this, what would have to be true for this hire to be the right call right now, and is it actually true?"

Why it works: naming the specific condition that would justify the hire, rather than debating the hire in the abstract, turns a values-laden question into a checkable one.

The premortem
"Imagine it's a year from now and [decision] turned out to be a mistake. Write the explanation of what went wrong, in detail, as if it already happened."

Why it works: framing the failure as having already happened, rather than as a hypothetical risk, produces far more specific failure modes than a generic "what could go wrong" question does.

The cut-or-keep audit
"We've built [feature] and sunk [time/cost] into it. Here's what we know about actual user demand for it: [data or lack of it]. Ignoring what we've already spent, should we finish it, or cut it?"

Why it works: explicitly instructing it to ignore sunk cost counters the single most common founder bias in this exact decision, continuing to fund something because of what's already been spent on it rather than what it's actually worth finishing.

Using AI as a Product Manager

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The outcome-validation check
"For each of these roadmap items: [list them]. State the specific user or business outcome each is meant to serve. If you can't state one clearly from what I've given you, flag it rather than assuming one."

Why it works: this surfaces roadmap items that were never actually tied to a validated outcome, dressed up as obviously worth doing because a stakeholder wants it.

The stakeholder-conflict map
"Here's what each stakeholder is asking for, and roughly why: [describe the positions]. For each, what outcome are they actually optimizing for underneath the stated request, and where do those outcomes genuinely conflict versus just sound like they do?"

Why it works: most stakeholder conflict is actually a conflict between outcomes, not personalities, and naming the underlying outcome each side is chasing turns a personality clash into a tradeoff you can actually discuss.

The assumption-risk audit
"This roadmap bet depends on the assumption that [state it]. Has this actually been validated, or are we assuming it because it feels true? If unvalidated, what's the cheapest test that would tell us if we're wrong?"

Why it works: naming the specific unvalidated assumption, rather than a general sense of risk, turns an abstract worry into a concrete, checkable test you can actually run before committing further.

Using AI as a Sales Leader

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The stage-evidence audit
"This deal is marked as [stage]. Here's what's actually happened in it: [describe stakeholders engaged, last interaction, timeline]. Does the evidence actually support this stage, or is it more likely one stage behind?"

Why it works: checking a stated stage against the specific evidence that stage is supposed to require catches deals that are sitting in a column out of habit rather than because they actually moved there.

The rep-pattern diagnostic
"Here's what I've observed in [rep]'s recent calls and deal notes: [describe it]. Based on documented research on what predicts success in complex sales, where does their pattern actually break down?"

Why it works: grounding the diagnosis in documented research on what actually predicts success, rather than a generic sense that the rep "needs to improve," produces a coaching point specific enough to actually act on.

The forecast-disagreement resolver
"[Person A] and [Person B] disagree on whether this deal should be forecast as committed. Here's the data both are looking at: [describe it]. Is this a disagreement about the facts, or about where each person draws the line for "committed"?"

Why it works: most forecast disagreements aren't actually about the data, they're about an unstated difference in where each person's personal bar sits, and naming that explicitly turns an unresolvable argument into a specific, fixable definition problem.

Using AI as a Solo Operator or Freelancer

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The capacity-fit check
"Here's a new project I'm considering: [describe it, timeline, scope]. Here's my actual current workload: [describe it]. Given this, does the project genuinely fit, or am I about to overcommit? Be specific about where the strain would show up."

Why it works: naming where the strain would specifically show up, rather than a vague overall gut check, forces an honest look at whether the calendar gap is really capacity or just an empty-looking week that isn't actually free.

The value translation
"This project will produce [describe the outcome, revenue impact, time saved, risk avoided] for the client. Translate that into a dollar estimate of value, not a rate based on my time, and suggest a price that reflects it."

Why it works: translating the outcome into the client's own financial terms usually produces a number well above what an hourly rate calculation would, since most solo operators default to pricing their time rather than the value the work actually creates.

The outreach-clarity test
"Here's an outreach message I'm about to send: [paste it]. If you were the recipient, what would you think I'm actually offering, and what's unclear or vague?"

Why it works: a rushed outreach message often reads as clear to the person who wrote it and vague to everyone else, and asking the AI to stand in as the confused recipient catches that gap before it costs a reply.

Using AI as an Agency Owner

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The scope-creep audit
"Here's the original scope of work: [paste it]. Here's the new request the client just made: [describe it]. Is this covered under the original scope, or does it warrant a change order? Be specific about which line it falls under or doesn't."

Why it works: most scope disputes happen because nobody checked the request against the actual written scope in the moment, and a specific, line-by-line answer is much harder to argue with later than a gut feeling.

The margin check
"This project is quoted at [price]. My best estimate of hours required is [hours] across [roles and rates]. Given our target margin of [percentage], does this project actually clear it? Show the math."

Why it works: a project can look profitable on the invoice and still be underwater once real hours are counted honestly, and forcing the actual math surfaces that gap before the contract is signed, not after.

The client-tier sort
"Here's what I know about each of these clients: [revenue, margin, hassle level, referral value, relationship history]. Sort them into keep, grow, or reconsider, and tell me which specific input is driving each call."

Why it works: naming which specific input, margin, hassle, referral value, is actually driving each tier forces a real answer instead of a vague overall impression, and it surfaces disagreements between inputs (high revenue, low margin) that a gut read tends to smooth over.

Why a Weekly Review Is a Good Use of AI, and How to Actually Run One

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The protected-hours audit
"Here's roughly how my week broke down: [describe it]. How many hours would you estimate were genuinely protected, focused time versus fragmented into meetings, messages, and interruptions? Be specific about where the fragmentation happened."

Why it works: naming an actual, if rough, number forces a concrete accounting rather than a vague sense of having been "pretty busy," which is the vaguer, less useful read most people default to.

The misallocation check
"I said [stated priority] mattered most this week. Based on what I've told you about how the week actually went, did it get the time that statement implies it should have? Where did the gap come from?"

Why it works: this directly tests the gap between stated priority and actual time allocation, which is usually where a week quietly goes wrong without anyone noticing until it's repeated for a month.

The carry-forward filter
"Of everything still open from this week, which items are actually still worth doing, and which have been carried forward multiple times without ever becoming urgent enough to finish? Be honest about the second category."

Why it works: an item that's been carried forward for three weeks without becoming urgent is usually a candidate for dropping entirely, and naming that pattern explicitly is easier than noticing it buried in a growing list you've stopped actually reading top to bottom each week.

The next-week commitment
"Given this week's actual pattern, what's the one change to how I protect time next week that would matter most? Not a list, one specific change."

Why it works: forcing a single priority change, rather than a list of good intentions, produces something that's actually more likely to survive contact with next week's inevitable pressure, since a list of five good intentions rarely survives past Tuesday.

Writing Difficult Emails With AI: A Method That Actually Works

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The outcome brief
"I need to write a difficult email about [situation]. Before drafting anything, tell me: what are the realistic outcomes I could be aiming for here, and what would each one require the email to actually say?"

Why it works: most difficult emails stall because the writer hasn't decided what they actually want, preserve the relationship, force a decision, or escalate, and are trying to write one email that vaguely accomplishes all three. Naming the outcome first forces the decision the blank page was avoiding.

The multi-draft request
"Write me three complete versions of this email: one that prioritizes preserving the relationship, one that forces a clear decision by a deadline, and one that escalates without sounding like an escalation. Full drafts, not summaries."

Why it works: comparing three finished options is a fundamentally easier decision than staring at one blank page and hedging, and it surfaces a register you might not have thought to try.

The tone check
"Read this draft as if you were the recipient, someone who is [describe them, defensive, busy, already annoyed]. What in this email would land badly, and what's the smallest change that fixes it?"

Why it works: writers are bad at judging their own tone under stress, and asking the AI to read as a specific type of recipient, rather than a generic one, surfaces genuinely different problems than a generic "is this too harsh" question does.

The read-aloud pass
"Read this draft back to me exactly as written, sentence by sentence, and flag anywhere the phrasing sounds unnatural if spoken out loud."

Why it works: written-sounding phrases that read fine silently often reveal themselves as stiff or over-formal the moment they're voiced, and this catches the specific artifact of AI-drafted prose that "sounds like an email" rather than like a person.

Delegation for Founders Who Can't Let Go

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The task-relevant maturity assessment
"Here's a task I keep doing myself: [describe it]. Here's who on my team might take it on and what I know about their track record on similar work: [describe them]. Help me assess their task-relevant maturity for this specific task and suggest an appropriate starting level of oversight."

Why it works: separating task-specific readiness from general trust in the person catches mismatches a gut-level "do I trust them" question tends to miss.

The outcome-only brief
"Here's a task I'm about to delegate and here's how I'd normally do it myself: [describe it]. Help me rewrite this as an outcome-only brief, stating what success looks like, without prescribing the specific steps or method to get there."

Why it works: founders who've done a task themselves for years tend to default to describing their own method out of habit, and a deliberate rewrite pass catches this before the brief goes out.

The commitment audit
"Here's my current list of active projects and commitments: [list them]. Help me identify which ones are creating delegation bottlenecks because there's genuinely too much total work, versus which ones just need better task-matching with the people I already have."

Why it works: distinguishing a volume problem from a matching problem prevents solving the wrong one, adding delegation process to a company that's simply taken on too much doesn't fix the actual bottleneck.

Feedback People Actually Hear

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The care-and-directness check
"Here's feedback I need to give: [describe the situation]. Help me write it so the care is specific and genuine, not generic warmth, and the critique is specific and actionable, not vague. Show me where I'm currently being vague on either side."

Why it works: most feedback drafts fail on one axis or the other without the writer noticing, and an explicit check against both catches the gap before the conversation happens.

The identity-versus-behavior rewrite
"Here's feedback I was planning to give: [paste it]. Rewrite it so every statement is about a specific, observable action rather than a trait or character judgment about the person."

Why it works: identity-level language is often unconscious, "you're careless" instead of "this specific thing was missed", and a rewrite pass catches it reliably.

The safety read
"Here's my working relationship with this person: [describe the history, including past hard conversations]. Based on this, how much groundwork-building should I do before delivering direct critical feedback, versus how ready is this relationship for directness now?"

Why it works: forcing an explicit read on relationship safety before choosing a feedback approach prevents defaulting to the same delivery style regardless of context.

Making Decisions You Can't Take Back

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The named-bias check
"Here's the irreversible decision I'm facing: [describe it]. Walk through the well-documented cognitive biases, loss aversion, sunk cost, overconfidence, recency bias, and tell me honestly which ones are most likely distorting my current read of this situation."

Why it works: naming the specific bias forces a deliberate, slow-thinking check rather than a generic "have you considered the downsides" prompt that doesn't target any particular failure pattern.

The inversion list
"Here's the decision: [describe it]. List every specific way this could fail badly, then tell me which of those failure modes I currently have the least real information about."

Why it works: inversion surfaces concrete risks a forward-looking "will this succeed" question tends to skip past, and ranking by information gap tells you exactly where to focus before deciding.

The disagreement simulation
"Here's my current lean on this decision and my reasoning: [describe it]. Argue the strongest case against my position as if you were someone with real, relevant experience who disagreed with me, and tell me what evidence would actually change your mind."

Why it works: simulating a genuine, well-informed disagreement surfaces weaknesses in your reasoning that a simple pro-and-con list tends to miss, because it forces engagement with the strongest counter-case rather than a weak strawman.

Meetings That Produce Nothing

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The purpose test
"Here's the meeting I'm about to schedule: [describe it]. Help me write one sentence defining what success looks like, either a specific decision made or a specific understanding reached, and tell me honestly whether the meeting as currently planned is actually built to produce that outcome."

Why it works: forcing a single-sentence success definition before the invite goes out catches the most common failure, a meeting with no clear purpose, before it ever happens.

The hats-round agenda
"Here's the topic for an upcoming meeting: [describe it]. Draft a two-round structure: a risks-and-concerns-only round, then an opportunities-and-upsides-only round, with explicit instructions that no one responds to another person's point until their own round is complete."

Why it works: a written structure for the rounds makes it much easier to actually hold the no-crosstalk rule in the room than trying to improvise it on the fly.

The hollow-agreement probe
"We just reached what looked like unanimous, fast agreement on this: [describe the decision]. Give me two or three specific, pointed questions I could ask the group to test whether this is genuine agreement or avoidance of disagreement."

Why it works: having specific questions ready, rather than a generic "does anyone disagree," makes it meaningfully harder for quiet avoidance to pass as consensus.

Negotiating When You Have No Leverage

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The hidden-leverage question set
"Here's the negotiation I'm in and why I have little formal leverage: [describe it]. Give me three calibrated "how" or "what" questions that might surface constraints on the other side I don't currently know about."

Why it works: calibrated questions are a specific skill, open-ended enough to invite real information, pointed enough to surface a genuine constraint, and most people default to yes-or-no questions that don't do this work.

The smallest-meaningful-concession
"Here's what I actually want from this negotiation and here's what seems realistically achievable right now: [describe both]. Help me identify the smallest concrete concession that would move me meaningfully closer to the full goal, rather than holding out for everything."

Why it works: naming a specific, smaller target prevents the common failure of holding out for an unrealistic full win and ending up with nothing at all.

The relationship-preserving close
"I'm not going to get what I wanted in this negotiation, but I want to preserve the relationship for a future round. Help me draft a close that states my position clearly without burning the relationship or sounding like I've simply given up."

Why it works: a close that's both clear and relationship-preserving is hard to write under the stress of a loss, and having one drafted in advance prevents an emotional, relationship-damaging reaction in the moment.

Pricing Your Work When Money Talk Is Hard

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The outcome-value estimate
"Here's the project: [describe it]. Here's what happens if it goes well: [describe the outcome]. Here's what it costs the client if it goes badly or doesn't happen at all: [describe the downside]. Help me put a rough value range on this outcome, separate from how many hours the work will take me."

Why it works: anchoring the price conversation to the outcome's value, not your time, is the entire mechanism behind value-based pricing, and most people never actually write the outcome's value down before quoting.

The offer stack review
"Here's what I'm planning to offer at this price: [list everything included]. Review this as if you were a skeptical buyer comparing it to a cheaper, vaguer alternative. What's unclear, what's missing, and what would make the value obviously worth the price difference?"

Why it works: a skeptical-buyer review surfaces the gaps in your offer before a real client finds them mid-negotiation, when it's much harder to add value without looking reactive.

The hours-question deflection
"A client just asked how many hours this will take instead of discussing the value we agreed on. Give me two or three ways to redirect the conversation back to the outcome without sounding evasive about the question."

Why it works: having a ready redirect prevents the conversation from sliding back into billable-hour logic in the moment, which is when it's hardest to resist doing so.

Procrastinating on the Work That Actually Matters

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The two-minute version
"Here's the project I keep avoiding: [describe it]. Help me define a version of the very first step that would take two minutes or less, small enough that there's no real excuse not to do it today."

Why it works: scaling the first action down to an almost trivially small size removes the size of the task as a reason to keep avoiding it, which is often the actual barrier.

The starting ritual design
"I need a reliable ritual that precedes starting this specific kind of avoided work: [describe it]. Suggest two or three simple, repeatable actions, tied to time, place, or a specific first sub-task, that could become the fixed trigger that precedes starting every time."

Why it works: a designed ritual removes the daily decision of whether to start, which is the decision most likely to go the wrong way under stress or low energy.

The deep work block audit
"Here's my current week's schedule: [describe it]. Help me identify a specific, protectable block of time for this avoided project, and list the concrete interruptions, notifications, specific people, recurring requests, that would need to be blocked for that time to actually hold."

Why it works: naming the specific interruptions in advance makes the boundary concrete and enforceable, rather than a vague intention to "focus" that collapses at the first notification.

Saying No to a Client Without Losing Them

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The self-orientation check
"I'm about to say no to this client request: [describe the request]. Before I respond, check whether my reason for declining is about protecting their actual outcome or about protecting my own time and margin. Write the no two different ways, one from each motive, and show me the difference in how they'd land."

Why it works: forcing the two versions side by side makes a self-oriented no obvious before you send it, which is the moment it's cheapest to fix.

The calibrated no
"I need to decline this request: [describe the ask]. Instead of a direct no, give me two or three "how" or "what" questions that hand the problem of this request back to the other person without me stating a flat refusal."

Why it works: a question keeps the other side in problem-solving mode instead of pushing them into defending the original ask against your rejection of it.

The scope line
"Here is the original agreement or scope: [paste it]. Here is the new request: [describe it]. Tell me precisely which part of the original scope this falls outside of, so I can decline by pointing to the agreement rather than arguing from how I feel about the request."

Why it works: a specific line in a document is much harder to argue with than a general sense that something feels like too much.