--- title: Try This — Saturday AI Thoughts source: https://steadman.ai/newsletters/david/try-this.html published: 2026-05-23 summary: Every practical tip from Saturday AI Thoughts, collected in one place with copy-and-paste prompts, filterable by category and searchable by keyword. --- # Try This Every practical tip from [Saturday AI Thoughts](https://steadman.ai/newsletters/david/), collected in one place with copy-and-paste prompts. Filter by category, search by keyword, copy for your AI tool of choice. 41 tips from 14 editions (22nd February to 23rd May 2026). Six categories: Quality & verification, Workflow & skills, Discovery & use cases, Personal practice, Leadership & adoption, Building. --- ## Edition 14: Kids these days (23rd May 2026) ### When the output goes wrong, shrink the task *Quality & verification* Justin Skycak put it as a principle for skill acquisition this week: shrink the unit of practice until the mistake has nowhere to hide. The same rule applies to working with language models. Sprawling prompts produce sprawling failures you can't diagnose. Break the task into its smallest meaningful unit, run it, inspect the output, then rebuild. If you can't immediately see where it went wrong, your chunk is still too large. #### Prompt: Option A - diagnose ``` The response above isn't quite right. Before I rewrite my prompt, help me decompose. Walk me through the task you just attempted as a sequence of the smallest possible sub-tasks. For each sub-task tell me (a) what specifically you did and (b) which sub-tasks you think are most likely to have caused the failure, with your reasoning. I'll then tell you which one we work on alone. ``` #### Prompt: Option B - pre-empt ``` Take the task above, break it into a series of individual prompts. I want to run them one by one next time so that I can more easily validate each step before moving on to the next. For each prompt, tell me what input I'll need to hand in and what output I should expect, so I can tell whether the step worked. ``` ### Ask AI questions it can't possibly know the answer to *Quality & verification* A marketing lead at a global firm told me this week she's running a five-minute stress-test on every AI tool she's thinking of trusting. She uploads her own data, asks the model to use only that data, then asks it questions she knows the data can't answer. She's learned what its confident-but-wrong mode looks like before depending on it for an answer she can't independently check. Worth doing once on every tool you rely on. #### Prompt: The impossible-questions stress-test ``` I'm going to stress-test you. Use ONLY the data and documents I've already shared with you in this conversation. Do not browse the web. Do not draw on your training data. Do not infer beyond what's explicitly in front of you. I'll ask you three questions. For each question, either give me the answer with the exact source location, or tell me explicitly that the materials don't contain the answer. Don't guess. Don't reach. Don't pad. Question one: [your first question - answerable from the materials] Question two: [your second question - answerable from the materials] Question three: [your third question - NOT answerable from the materials] ``` ### Run your day past AI before you start it *Workflow & skills* A senior leader described her commute habit to me this week. She opens Claude, asks it to review her calendar and her email, then asks it to surface what she needs to read before each meeting, what's carrying over from yesterday, and which emails in her inbox need replies before the day eats her. Five minutes on the train, and the day is scoped from outside her own head. \"Just a nice little daily habit,\" she said. Try it tomorrow. #### Prompt: The morning prep prompt ``` Review my calendar for today and my email inbox for the last 48 hours. Then walk me through the day in three sections. (1) Pre-reads. For each meeting on the calendar, what should I read or recall first, and what's the single most useful question I should arrive with? Pull context from any email threads tied to the meeting if you can see them. (2) Carry-overs. Which open threads or commitments from yesterday's calendar and email are likely to collide with today's schedule? Anything I said I'd do that I haven't, and that today is the last clean window for? (3) Email replies. Which unanswered emails in my inbox need replies before 11am, in what order, and what's the key point each reply needs to make? Be specific. Time-box each item if you can. Flag any conflicts I should resolve before 9am. ``` ## Edition 13: What boards accept (16th May 2026) ### Hand over the context, not just the question *Workflow & skills* Experienced leaders have context. They're short on time. AI tools convert context into time saved, but only if you hand the context over. Most don't. They type questions ("how should I structure a board paper on Q2?") and get general answers ("a clear narrative arc, three key insights, a risk slide"). Useful, but the leader still writes the paper. A leader I spoke with this week made the leap from asking to delegating. "Here are the Q2 numbers, last quarter's board paper, and the three things the board flagged. Draft it." Same model, same minute. Asking gets you an outline. Delegating gets you a draft. #### Prompt: The "draft this thing for me" version ``` Here's everything you need: - [Paste the data, numbers, source material] - [Paste prior versions, last year's equivalent, or context the reader expects] - [Paste the three things you know your audience will ask or care about] Draft the [board paper / proposal / memo / email / one-pager] for me. Match the tone of [the prior version / the audience's usual register]. Show me your draft first. Then I'll come back with corrections. ``` #### Prompt: The "do the analysis for me" version ``` Here's the data: [paste the spreadsheet, transcript, document, or context]. Do the analysis. Specifically: 1. Find the three most actionable patterns. 2. Tell me which one I should prioritise, and why. 3. Show your working: what you considered, what you ruled out, where your confidence is thin. Don't ask me clarifying questions until you've given me your first answer. Then I'll come back. ``` ### Build a personal skill, and add a rule to it every Sunday *Workflow & skills* A person I worked with this week reviews 100-page reports from their team on Sunday nights. Typos, inconsistent language, logic gaps. A simple review skill in Claude now catches all those. The compounding version is their own preferences layered on top: this market "is expected to grow" not "will grow"; never "dropping precipitously." Every time they catch a miss the model didn't, they open Claude and say "add this rule to my personal review skill." Whatever skill you build, the compounding habit is the same. Catch what the model missed. Add the rule. Trust the skill more next week. #### Prompt: Starter review skill template ``` Role: Review the document I attach against my preferences below. Return a numbered list of suggested changes, by page number, with the recommended fix and the reason. Do NOT edit the document. The human keeps the pen. Preferences: - A market "is expected to grow," never "will grow." - Avoid "dropping precipitously," "skyrocketing," "soaring." - Cite percentages with a year and a source. - Flag any sentence over 30 words. - [add your own rules here as you go] Output format: Page N: [the issue] → [the suggested change]. [The reasoning, one sentence.] ``` #### Prompt: The compounding habit ``` Add this rule to my personal review skill: [paste the rule you just caught that the skill missed]. From now on, flag this whenever it appears in any document I send you. ``` ### Schedule a daily AI briefing. The use cases will follow. *Building* AI tools sit closed until you open them. That's a real reason senior leaders bounce off: not bad prompts, but a tool that requires you to think of the use case first. Scheduled tasks invert this. Every morning at three, mine reads my inbox since the evening before, prepares one-paragraph briefings for every meeting on the day's calendar, and emails me a short summary that takes three minutes to read with breakfast. The tool changes from something you open to something that opens your day. Suggested time: 3am UK so the briefing is waiting when you wake. 10pm US East Coast for similar timing. #### Prompt: The daily briefing schedule ``` Every weekday at 03:00 UK, do the following and email the result to me at [my address]: 1. Read everything in my inbox since yesterday at 18:00. Summarise the threads I'll most need to act on today, with thread links. 2. Read every meeting on my calendar for today. For each, prepare a one-paragraph briefing using: - The most recent email thread with the attendees (if any). - Any agenda or notes in the meeting description. - One paragraph of public news on the topic from the last 48 hours. 3. Write the whole thing as a single email I can scan in three minutes. Use bullet points sparingly. ``` ## Edition 12: Choosing is the work (9th May 2026) ### Push it harder, then skillify, then iterate *Workflow & skills* The simplest workflow upgrade I've coached this year, and it stacks. Push it harder: when the model gives you a perfectly reasonable answer, tell it to do more searches and use credible sources. Three extra words. The next round does real research, cross-references government sources, and surfaces what the first pass skipped. Skillify it: when a session produces something useful, ask the AI to turn the exchange into a reusable skill, a folder with a description and a prompt any future session can pick up. Iterate: when the skill misbehaves, just tell it "change yourself so X." It rewrites itself in place. Works particularly well in Claude, where skills are first-class. #### Prompt: Push it harder ``` That's a draft. Now do the work properly. Run more searches. Use credible primary sources where you can find them, including government statistics, peer-reviewed papers, and reports from named firms. Cross-reference the claims that matter most. Show the sources alongside the conclusions and tell me which ones you'd trust if you had to act on this tomorrow. ``` #### Prompt: Skillify the session ``` Treat this session as raw material for a skill. Run a retrospective: what was I trying to do, what worked, what didn't, what shortcuts or guardrails would have produced a better answer faster. Then turn the retrospective into a skill: a short description that tells future sessions when to use it, and a prompt that captures the working approach. Where I made mistakes during the session that the final answer corrected for, encode those mistakes as guardrails in the prompt so the skill doesn't repeat them. ``` #### Prompt: Iterate by talking to the skill ``` Open the [skill name] skill. Ask me questions about how I'd like it to be different. Then proactively suggest ways it could be better, more reliable, bolder, or more effective, and carefully implement the ones I approve. ``` ### Shadow your most AI-pilled employee for two days *Leadership & adoption* Most senior leaders consume AI through polished demos and vendor decks, then make strategy decisions with no felt sense of the technology's real constraints. The cheapest fix costs nothing. Find the right person. Not the keenest, not the loudest, not the head of digital transformation. The "rabbit-holed" colleague. Signs they're the right person: Then do the same work yourself for a week. Shadowing alone is anthropology. The point is to use the tool the way they do, on your own work, for long enough that the muscle memory shifts. #### Prompt: Questions to ask while you shadow ``` Walk me through the first thing you opened today and why. Which AI tools and apps and settings are you using? Show me the prompt you actually typed. Don't tidy it up. What did the model get wrong on first pass, and how did you push back? What did you stop doing manually three months ago that's still in my diary? What do you wish other people would stop doing manually? ``` ### Ask AI to build you an HTML slide deck instead of PowerPoint *Building* Just ask. "Build me a slide deck on [topic] in a single HTML file." Most chat tools can do this now. Why bother? PowerPoint output from AI is uneven, with broken chevrons, mis-rendered shapes, and two hours of touch-up. HTML slides come back closer to ready, are infinitely easier to edit (you just talk to the file), and unlock things PowerPoint can't do: an inline edit button, an AI-spoken narration, a presenter view that asks the model questions during Q and A. The basic ask works on its own. The extras turn the deck into something people remember. #### Prompt: The basic prompt ``` Build me a slide deck on [your topic] in a single HTML file. Sixteen-by-nine, big readable type, one idea per slide. Use Tailwind CSS via CDN for styling. Add tasteful keyboard navigation: arrow keys to advance, Esc for an overview grid, F for full screen. Include a small floating progress indicator. Serif headings, sans body, generous whitespace, no clutter. If a slide would normally have a chart, build it as inline SVG so it stays editable. End with a contents slide I can use as a hub. ``` #### Prompt: The richer prompt (with extras) ``` Same brief as above, plus: (a) Add an "edit mode" toggle in the top right that turns every text element on the deck into a contenteditable. Changes persist in localStorage so I can refresh and keep them. (b) Add a "speaker view" page at /speaker.html that shows the current slide, the next slide, my notes for the current slide, and a running timer. Hot keys to advance from there too. (c) Build a "narrate" button per slide that reads the slide's notes aloud using the browser's built-in speech synthesis. Voice picker. (d) Build a "Q and A" page that takes a typed question, looks at the deck content, and proposes an answer based only on what's in the deck. If the question is out of scope, say so honestly. (e) Make every chart inline SVG with editable values in a small JSON block at the top of the file, so I can tweak numbers without touching the chart code. (f) Use brand tokens in CSS variables at the top of the file. Make it trivial to recolour the deck by changing two or three values. ``` ## Edition 11: The bill and the harness (2nd May 2026) ### Pick one tool, get good, then refine your harness *Workflow & skills* Pick one AI tool. Don't worry about which. Get fluent. Then spend the next month making it yours. That means skills, saved instructions, project context and tools it can reach. The model will keep improving on its own. The harness is your job. A leader I spoke to has spent weeks running the same task through ChatGPT and Claude side by side, then asking each to review the other. Genuinely interesting and occasionally useful, but they can't decide which to use when. The advice from someone who has been at this longer: pick one, become very good with it, learn its quirks, then build the layer above it that reflects how you actually work. Most of the value is in fluency with one tool, not coverage across two. #### Prompt: Build your harness ``` I use [tool name] for my work in [your field/role]. I've been using it for [time period]. Help me build the layer above the model that reflects how I actually work. Ask me: 1. What are the five tasks I do most often with AI? 2. For each, what do I wish it knew about my preferences without me telling it every time? 3. What does "good" look like for my most common output types? 4. What mistakes does the model keep making that I keep correcting? Then write me a set of custom instructions, three reusable skill descriptions, and a project context file I can save. Make each one specific to my answers, not generic. ``` ### Force yourself to change something on every AI output before you ship it *Quality & verification* Whatever the AI gives you, a paragraph, a slide, a summary, add something, remove something, or reorder something before it leaves your hands. Make it a rule, not a judgement call. This came up at a senior training session, as the room debated when the "check, edit, own" model breaks down. Increasingly the edit feels optional because the output looks good. The rule of "always change something" is a forcing function. It keeps your fingerprints on the work, keeps you reading carefully enough to find the change worth making, and prevents the slow drift towards accepting output uncritically. #### Prompt: The CEO checklist ``` Before I send this, run the CEO check (Check, Edit, Own): 1. CHECK: Read it as if a sceptical colleague wrote it. Flag any claim you can't verify, any number without a source, any assertion that sounds confident but vague. 2. EDIT: Suggest three specific changes I should make to put my fingerprints on this. Not cosmetic changes. Changes that improve the argument, sharpen the evidence, or remove something that doesn't earn its place. 3. OWN: If this went out under my name and someone challenged the weakest point, what would I say? If I can't answer that, flag it. ``` ### Skip the slides, build the page *Building* Next time you'd reach for PowerPoint to walk a group through a framework, write the content as text and ask your model to turn it into a single HTML page instead. In a senior strategy session this week, the most-praised artefact in the room was not a deck. It was a web page someone had built to walk teams through their thinking. Building it took a clear thought, a text file, and a small Claude skill they had set up once. Their harness paying off in front of the room. #### Prompt: Turn a framework into a web page ``` I have a framework I want to walk a group through. Here it is: [Paste your framework, outline, or key points] Turn this into a single HTML page that tells the story step by step. Clean, readable design. One idea per screen section. Smooth scroll between sections. Use a serif font for headings and a sans font for body text. No clutter. If any section would benefit from a simple diagram or chart, build it as inline SVG. Make it something I'd be proud to screen-share in a meeting. ``` ## Edition 10: Rise of the auditors (25th April 2026) ### Ask "is this the simplest version?" before accepting AI output *Quality & verification* Language models have no incentive to simplify. Work is free to them. Bryan Cantrill calls it the loss of "laziness": the human impulse to find the crisp abstraction rather than add another layer. When AI drafts something, check whether it found the simplest solution or the first solution. A three-step process wrapped in seven steps of hedging is worse than the three steps alone. The scarce resource now isn't production. It's restraint. #### Prompt: The simplification test ``` Here's something I just produced with AI: [Paste output] Before I accept it, simplify it. Not summarise. Simplify. Find the core argument or solution buried inside it. Strip away: hedging, redundant steps, unnecessary qualifications, and anything that exists because production was free rather than because it was needed. Show me the simplest version that still does the job. Then tell me what you removed and why none of it was load-bearing. ``` ### Audit cold *Quality & verification* When checking AI output, open a new chat with a different model. Upload the source materials and nothing else. Don't verify inside the same conversation that generated the work. That conversation will defend its own output. I've tested this repeatedly over the last month: the same model that produced a confident answer will find the errors when it reads the sources fresh, without its own prior reasoning in the context window. Sullivan and Cromwell filed hallucinated citations in a multibillion-dollar bankruptcy case. This would have stopped it. #### Prompt: The cold audit prompt ``` I need an independent review of this document. Important context: I did not write this and you did not write this. You have no stake in it being right. Read it cold. Here are the source materials it should be based on: [Paste sources] Here is the document: [Paste document] For every factual claim: (1) is it supported by the sources? (2) does it overstate what the sources say? (3) are there gaps where the sources say something important that the document ignores? (4) are there any numbers, names, dates, or citations that don't match? Give me a numbered list of findings, ordered by severity. ``` ### Save one reusable AI workflow this week *Workflow & skills* Google shipped Skills in Chrome this week: saved one-click AI prompt workflows that run on whatever page you're viewing. Pick a task you do weekly. Summarising a website, extracting action items from meeting notes, comparing options across open tabs. Save it as a named Skill. Whether you use Chrome, Claude, or something else, the principle is the same: if you've done it three times, encode it. Each saved workflow compounds. Each unsaved one gets reinvented from scratch. #### Prompt: Capture a workflow as a reusable skill ``` I did [describe the task you did this week with AI]. It worked well. Help me turn it into a saved workflow I can reuse. I need: 1. A name for it (something I'd recognise in a list) 2. A one-line description of when to use it 3. The steps as a numbered checklist 4. The prompt or prompts I should paste each time 5. Any context or inputs I need to gather before starting Make it something I can hand to a colleague who's never done this task before. ``` ## Edition 9: The proxy break (18th April 2026) ### When helping someone find AI use cases, ask what keeps them awake, not how AI can help *Discovery & use cases* One produces a polite, dull list. One produces a bold list of creative use cases. The difference is the question being asked. "How can AI help?" invites safe answers. "What keeps you awake at night?" surfaces real problems that happen to have AI solutions. Concrete, named, immediately applicable. #### Prompt: Prepare a use-case discovery conversation ``` I'm about to have a one-on-one conversation with a senior leader about AI opportunities in their area. They work in [describe their role/department]. They've agreed to the meeting but I can tell they're sceptical. Help me prepare: 1. Three opening questions that get them talking about their real frustrations at work without mentioning AI 2. A listening framework for spotting where AI maps onto what they describe 3. A way to bridge from their pain point to a concrete AI experiment they could try this week, framed as solving their problem, not as "adopting AI" ``` ### Let the model research you before you write its instructions *Workflow & skills* I sat with a senior leader this week who is getting great results from AI despite not having filled in his custom instructions. Don't make this mistake. It helps every single response to be better for you. Rather than manually writing them, start a new chat and type the prompt below. Revisit it regularly. Most people write something generic and never look back. #### Prompt: Self-research and write custom instructions ``` I am [your name]. I work at [your organisation] as [your role]. Search the web to learn about me, my work, my company, and my industry. Then write me a set of custom instructions I can save that would cause you to work well with me in future. Cover: my communication style preferences, the kind of work I do, the standards I hold, common tasks I'd bring to you, and any context that would make your responses more useful. Write it in second person ("You are working with..."). I'll edit it from there. ``` ### Find out where your AI value actually sits *Discovery & use cases* Answer our five questions about where you think AI value is for your team or your organisation, then five about how much of that value you've captured so far. You get a chart showing you where the opportunities are. It's where I think most firms are: capturing about 16% of the value they can see, with almost everything still on the table. #### Prompt: Prepare for the AI Value Map ``` Before I use the AI Value Map diagnostic, help me calibrate my thinking. I need to assess AI value across five phases for my [team/department/organisation] which does [describe work]. Ask me one question about each phase: 1. Individual productivity: the hundred small things 2. Team standards: from conversation to architecture 3. Process orchestration: "AI does the process" 4. Role and team redesign: different shape, not same people faster 5. New revenue: do different things, not the same things faster After I answer, tell me where you think the biggest unrealised opportunity is and why. Then I'll take the full diagnostic at steadman.ai/newsletters/david/ai-value-map.html ``` ## Edition 8: What a day can do (11th April 2026) ### Start with critique, not creation *Leadership & adoption* The brand voice skill built for a luxury goods team was deliberately limited to diagnosis: it reads copy and flags where it departs from seven writing rules. It doesn't rewrite anything yet. Teams fear the proofreader far less than the replacement. Once they trust the critique, generation follows naturally. If you're building AI tools for a team, don't start with "write this for me." Start with "tell me what's wrong with this." Nobody fights the spellchecker. #### Prompt: Build a critique-first AI tool ``` I want to build an AI quality checker for my team's [type of work, e.g. marketing copy, client reports, proposals]. Instead of generating content, it should only critique existing work. Help me define: 1. The 5-7 specific rules or standards our work should meet (ask me questions to surface these) 2. A prompt that reads a piece of work and flags every place it departs from these rules 3. A scoring system (e.g. how many rules are met) 4. A way to explain each flag so the person learns, not just corrects The goal is a tool the team trusts before we ever ask it to write anything. ``` ### Ask what keeps people up at night, not what they want AI to do *Discovery & use cases* During AI onboarding conversations, the first question ("what do you use AI for?") produces a predictable list. The second ("forget AI, what's harder than it should be?") produces the real use cases. The first question surveys existing habits. The second surfaces unmet needs. Almost nothing appears on both lists. Run them. Compare. #### Prompt: Design a use-case discovery session ``` I'm running an AI use-case discovery session with [team/department]. I have [time available]. Help me design it: 1. Five opening questions that get people talking about what's genuinely hard about their work, without mentioning AI at all 2. Five follow-up probes that connect their pain points to AI capabilities without being leading 3. A template for capturing each use case: the problem, the current workaround, who it affects, and what "solved" would look like 4. A way to prioritise the use cases by impact and feasibility after the session ``` ### Show your team how others use AI. That alone may double the impact *Leadership & adoption* An experiment with 515 startups found that simply showing half of them case studies of how other startups use AI led to 44% more usage, 1.9x higher revenue, and 39% less capital needed. The friction isn't accessing the tools. It's discovering where AI creates value in your specific work. The researchers call it "the mapping problem." The intervention was cheap (case studies), the effect was large. If you manage a team, the most effective thing you might do this week isn't training. It's sharing three examples of how other teams in your industry are using AI. #### Prompt: Create case studies for your team ``` I manage a team in [your industry/function]. I want to show them concrete examples of how other teams like ours are using AI. Help me find and write up three case studies. For each, I need: 1. What the team does 2. What they tried with AI 3. What worked and what surprised them 4. One specific thing my team could try this week Use real, named examples where possible. Search for recent cases. Make them specific enough to be credible, not generic "Company X increased productivity" claims. ``` ## Edition 7: What is your organisation actually for? (4th April 2026) ### Before you build anything, have AI interview you first *Discovery & use cases* Don't describe what you want and ask AI to build it. Instead, ask the model to interview you: the prompt below. It probes edge cases, surfaces assumptions and tightens scope before a line of work begins. Many ideas aren't as clear or well thought through as you think they are. Better to discover that in a five-minute conversation than a five-hour build. #### Prompt: The discovery interview ``` I want to build [describe your project or idea]. Before we start, interview me. Ask me every question you need answered before writing a single line of work. Cover: who this is for, what problem it solves, what "done" looks like, what constraints exist, what I've tried before, and what I'd consider a failure. Ask one question at a time. Don't start building until you've run out of questions. ``` ### Before you analyse anything, ask AI what looks weird *Discovery & use cases* Next time someone sends you a spreadsheet, a report or a set of financials, upload it and ask AI what looks unusual. I coached a finance professional this week who receives portfolio company P&Ls regularly. Before she even opens the numbers now, AI flags the things worth checking: an unusually high margin, a budget assumption that changed between the original and revised forecast, a line item that doesn't match the pattern. Five or six flags in thirty seconds. She still does the analysis. But she starts it knowing where best to look. #### Prompt: The anomaly scan ``` Here's a [spreadsheet/report/set of financials/dataset]: [Paste or upload your data] Before I start my analysis, read it and tell me what looks unusual. Flag: unexpected patterns, numbers that don't match, assumptions that seem wrong, anything that breaks from the trend, and anything you'd want to double-check if you were the person signing off on this. Give me 5-8 flags, ranked by how much they'd change the conclusions if they turned out to be wrong. ``` ### Don't build an app. Let AI be the app *Building* I coached a person this week who wanted to build a web application for an investment screening workflow. The build was simpler than they expected: rebuild the process as a repeatable skill inside an AI tool instead of building it as a standalone app. The AI itself becomes the application. The advantage is resilience: if the input format is wrong or a step fails, the AI adapts on the fly. A standalone app just stops. If you have a multi-step workflow you keep wishing someone would build software for, try describing it to your AI tool and asking it to turn the process into something you can re-run with one command. #### Prompt: Turn a workflow into a skill ``` I have a multi-step workflow I keep doing manually: [Describe the workflow step by step] Instead of building software for this, convert it into a set of instructions, decision rules, and output templates that I can invoke with a single phrase like "run the [name] workflow". Include: 1. What inputs it needs from me each time 2. What decisions it makes automatically 3. Where it should pause and ask me 4. What the output format looks like 5. How to handle edge cases (wrong input format, missing data, ambiguous results) ``` ## Edition 6: The system and the surrender (28th March 2026) ### Don't fact-check AI in the same conversation *Quality & verification* In a single chat, the model has its full reasoning chain in context and will tend to defend its conclusions when challenged. Start a fresh conversation, upload the same source materials, and prompt it to critique the output cold. I've started doing this for every high-stakes document. The independent perspective is materially more likely to find gaps. A false sense of verification is worse than no verification at all. #### Prompt: The cold fact-check ``` I need you to fact-check this document. Important: I did NOT write this and you did NOT write this. Read it cold. Here are the source materials it should be based on: [Paste sources] Here is the document: [Paste document] For every factual claim in the document: (1) verify it against the sources, (2) flag anything stated as fact that isn't in the sources, (3) flag any numbers that don't match, (4) flag any claims that go beyond what the sources support. Be suspicious. Your job is to find what's wrong, not confirm what's right. ``` ### Give your AI reviewer a persona with skin in the game *Quality & verification* Asking a language model to "check this for errors" produces generic feedback. Assigning it a specific sceptical expert persona, someone with domain expertise and institutional incentives to be unimpressed, produces something qualitatively different. I ran a quality-control pipeline this week where six review agents were given a senior partner persona. All six independently converged on the same systematic error class that a neutral reviewer had missed. The persona defines what "good" looks like. A generic prompt doesn't. #### Prompt: The persona review prompt ``` You are [name and role, e.g. "Sarah Chen, CFO of a FTSE 250 company"]. You have [describe their experience and what they care about]. You are known for [describe their reputation, e.g. "asking the question nobody wants to answer"]. You are reading this document because [reason]. Read it now and give your honest, unfiltered reaction: 1. What would you challenge in the next board meeting? 2. What would you praise? 3. What would make you worried? 4. What's missing that someone in your position would expect to see? Write your response in first person, in character. Don't break character. ``` ### After every good session, turn it into a reusable skill *Workflow & skills* When you've just completed a task you're pleased with, ask the model to turn it into a set of instructions, templates and process steps it can invoke next time with a single phrase. The law firm partner's 2,000-word prompt didn't happen in a flash of inspiration. It was built through iteration. Capturing what "good" looks like the moment you've achieved it, before the memory fades, turns a one-off win into a standing procedure. The output matters today. The instructions compound forever. #### Prompt: The skill capture prompt ``` I just had a productive session. We did [describe what you accomplished]. The result was [describe the quality of the output]. Run a retrospective: 1. What was I actually trying to do? 2. What approach worked? 3. What went wrong or took too long? 4. What shortcuts or guardrails would have produced a better answer faster? Then turn this into a reusable skill: a short description that tells future sessions when to use it, and a prompt that captures the working approach. Where I made mistakes during the session that the final answer corrected for, encode those mistakes as guardrails so the skill doesn't repeat them. ``` ## Edition 5: Reckoning and slope (21st March 2026) ### Mine your own email archive *Personal practice* Before writing a strategy document this week, I pulled 31 emails between myself and a client from the past six months and asked Claude to compress them into a structured set of questions and insights. Scattered observations, early instincts, half-formed frameworks: they became a clear argument in minutes. The emails already contained all the thinking. They just needed synthesising. Pick a topic you've been emailing about for months. You'll find you've already done more thinking than you realised. #### Prompt: Synthesise an email thread ``` Here are [number] emails between myself and [person/team] over the past [timeframe] about [topic]: [Paste emails] Don't summarise them chronologically. Instead: 1. What is the core question or problem that runs through all of these? 2. What positions or instincts appeared early that turned out to be right? 3. What assumptions shifted over the exchange? 4. What remains unresolved? 5. Compress the entire exchange into a structured brief: the question, the evidence, the options, and the recommended next step. ``` ### End every AI session the way a developer commits code *Workflow & skills* Close each working session with one line: the prompt below. Without it, context evaporates and the next session starts from scratch. With it, the session becomes a self-contained unit of work that can be picked up, forked or handed off. It sounds small. The cost of not doing it only becomes visible the next time you open the conversation. #### Prompt: The session commit ``` We're finishing this session. Before we close, document everything someone picking this up tomorrow would need: 1. What we were trying to do 2. What we decided 3. What we built or produced 4. What's still unfinished 5. What I should ask you to do first next time 6. Any files, links, or context that would be needed Write it so a future session could resume this task without asking me to repeat anything. ``` ### Use AI to teach you, not just to do things for you *Personal practice* Bloom's two-sigma result is the best-known finding in education research: one-to-one tutoring consistently moves students from the 50th to the 98th percentile. It's never been economically available except to the very wealthy. It now costs a monthly subscription. Pick one skill you want to add and spend twenty minutes a day asking AI to train you in it: run you through drills, quiz you, critique your answers. Most people use these tools to produce output. The people I know that impress me most are using them to produce understanding. #### Prompt: Start a personal tutoring programme ``` I want to learn [skill]. I'm currently at [describe your level]. My goal is to be able to [describe what competence looks like]. Act as my tutor. Start with a diagnostic: ask me 5 questions to understand what I already know and where my gaps are. Then design a 20-minute daily drill I can do for the next two weeks. After each session, quiz me, critique my answers, and adjust the difficulty. Be demanding. I'd rather struggle and learn than coast and feel good. ``` ## Edition 4: The power and the care (14th March 2026) ### Before you send AI work to anyone, simulate the toughest person who will read it *Quality & verification* An advisory team I work with creates AI versions of each board member, based on known priorities and past questions, and runs every document past these simulated reviewers before a human sees it. The virtual panel flags objections, tests assumptions and catches blind spots. The cost is an hour of AI time. The benefit is walking into the meeting having already rehearsed the hard questions. #### Prompt: Simulate a tough reader ``` I'm about to send this document to [describe the audience, e.g. "our board", "a sceptical client", "a senior partner"]. Before I do, simulate the toughest person who'll read it. Their profile: [describe them, e.g. "a private equity partner who has seen hundreds of these, values brevity, hates jargon, and will immediately check the numbers"]. Read my document as that person. Give me: 1. The three questions they'll ask first 2. The two weakest claims they'll challenge 3. Anything they'll find patronising or obvious 4. The one thing that might genuinely impress them Here's the document: [Paste document] ``` ### Delete the headline. Ask AI what it should be *Quality & verification* A manager at a training session showed me a quality check I hadn't seen before. He copies a colleague's slide, removes the headline and asks the language model what the headline should be based on the content alone. If the model's headline differs substantially from what the colleague wrote, it reveals a disconnect between what the slide says and what the person intended. A ten-second test that catches the gap between the message you're claiming and the evidence you're showing. #### Prompt: The headline test ``` Here's a slide with the headline removed: [Paste slide content without the headline] Based only on the content, what should the headline be? Give me three options: 1. One that summarises the data 2. One that makes a recommendation 3. One that tells the audience what to do next Then tell me: if the original headline was "[paste original headline]", does it match what the slide actually says? If not, what's the disconnect? ``` ### Fix the instructions, not just the output *Workflow & skills* When AI produces a mistake, most people correct it and move on. The fix disappears with the conversation. After every session where the result misses the mark, update the instructions that generated it: the custom instructions, the project brief, the context files. Every failure becomes a permanent improvement. The output matters today. The instructions compound forever. #### Prompt: Diagnose and fix the root cause ``` I just finished a session where the AI got [describe what went wrong]. The output I wanted was [describe]. The output I got was [describe]. Here are the instructions I gave it: [Paste your custom instructions or project context] Diagnose why the instructions produced the wrong output. Then rewrite the instructions so this specific failure can't happen again. Show me exactly what you changed and why. ``` ## Edition 3: Extraction or expansion (7th March 2026) ### Run AI and a human on the same task, then focus on the disagreements *Quality & verification* A manager this week ran the same research brief through both AI and a human team. The overlap was 78%. But the value wasn't in the overlap. It was at the edges: the surprising findings that only one method surfaced. Where human and machine agreed, the team moved fast with confidence. Where they disagreed, they'd found the questions worth investigating. The delta between AI output and human output is where insight lives. Try it on your next research task, competitive scan, or document review. #### Prompt: Run the comparison test ``` I'm running a comparison test. I gave the same research brief to both a human analyst and an AI tool. Here is the human output: [Paste human output] Here is the AI output: [Paste AI output] Don't tell me which is better overall. Instead: 1. List every point where they agree 2. List every point where they disagree or one found something the other missed 3. For each disagreement, tell me which source is more likely to be right and why 4. Identify the three most valuable insights that only appeared in one output Those disagreements are where I should focus my attention. ``` ### Know where your organisation sits on the AI tools landscape *Leadership & adoption* I've put together a short page mapping what I think of as Generation 1 versus Generation 2 AI tools, and why the distinction matters for how you invest in your people. The short version: many organisations are still stuck on constrained, default tools and most don't have anyone on Agentic / Frontier Generation 2 tools. I see two strategies working in parallel. For the many: move less-engaged people from free, constrained tools to competent use of good general-purpose applications. For the best: accelerate your top people with agentic, frontier tools and let them build entire workflows that deliver outsized impact. See the full explainer. ## Edition 2: The hundred small things (28th February 2026) ### Run your day through AI at six in the evening *Personal practice* At the end of each day, take whatever you captured (meeting notes, voice recordings, emails) and ask Claude for a five-paragraph reflection. Not a summary. A reflection. I've been doing this daily for months. The output compounds. You start noticing patterns you'd never have seen without the habit. The raw material is irreducibly yours. AI helps you see the shape of it. #### Prompt: The evening reflection ``` Here are my notes, voice recordings, and emails from today: [Paste content] Don't summarise them. Reflect on them. Write five paragraphs: 1. What the day was actually about (not what was scheduled) 2. What patterns you notice across the different activities 3. What I seem to be avoiding or postponing 4. What surprised you 5. One question I should sit with overnight ``` ### Before you build training, check the settings menu *Leadership & adoption* Most people never change the default model on AI platforms. Before investing in an elaborate training programme, walk up to five people in your organisation and ask two questions: which AI model are you using, and have you changed anything in the settings? The answers will tell you more about your organisation's AI maturity than any survey. If your people haven't opened the settings menu, the barrier isn't skill. It's something simpler: nobody showed them it was there. #### Prompt: The quick AI maturity audit ``` I'm about to design an AI training programme for my team of [number] people in [function/industry]. Before I do, help me build a quick audit. Write me a 5-question survey I can ask face-to-face (not email). The questions should surface: 1. Which AI tools they use and which model 2. Whether they've changed any settings or written custom instructions 3. Whether they've saved any reusable workflows or skills 4. What their biggest frustration is with AI tools 5. One task they wish AI could do but haven't figured out how Make the questions feel like a casual conversation, not an audit. Then tell me how to interpret the answers: what do the patterns reveal about where training should focus? ``` ### Push record. Think aloud. Send it to AI *Personal practice* When someone has stories they want to capture but can't easily write, skip the blank page. Push record while doing actual work. Don't write, don't perform, just think aloud. Send the raw recording to a transcription tool, then run the transcript through Claude. The stories that would otherwise stay untold (because the effort of writing them was too high) get captured without friction. I've seen this work for client case studies, internal knowledge sharing, and personal reflection. The raw material is human. The refinement is machine. That's the order that works. #### Prompt: Process a raw voice transcript ``` Here's a raw transcript of me thinking aloud about [topic]: [Paste transcript] This isn't polished. It's me working through ideas in real time. 1. Extract the core argument 2. Identify the three strongest points and the two weakest 3. Restructure it into something I could share with a colleague 4. Keep my voice. Don't make it sound like a report 5. Flag anything where I contradicted myself or where my reasoning has a gap I should fill ``` ## Edition 1: The wonder and the weight (22nd February 2026) ### Find your misfits (and back them) *Leadership & adoption* Identify the one-in-ten people in your organisation who are already at the forefront of using AI. The ones who experiment on their own time, who volunteer for pilots, who can't stop showing colleagues what they've built. Free them from at least some of their existing reporting lines. Give them the best tools. Bring them together, give them a name, give them a mandate to have an impact beyond their old job description. Most training programmes spread investment evenly. I don't think that's right. Your top ten percent will generate eighty percent of the value. A power user with the right support can transform a team. A reluctant user with mandatory training may forget everything by Thursday. #### Prompt: Identify your power users ``` I lead [describe your team/organisation] with [number] people. I want to identify the top 10% who are furthest ahead with AI: the ones who experiment on their own, volunteer for pilots, and can't stop showing colleagues what they've built. Help me design a simple 5-question diagnostic I can use in a team meeting or 1:1 to surface who they are without making anyone feel singled out. Then suggest three concrete things I could do in the next two weeks to: 1. Free these people from at least some of their existing reporting lines 2. Give them the best available AI tools 3. Give them a mandate to have impact beyond their old job description ``` ### Open Claude Code and build something *Building* Just build something. Pick a website you wish existed, or some data you wish was gathered across the web. Or an email scanning automation that summarises stuff for you on a schedule. Or an app that lets you do something you've been meaning to do. The only blocker is your imagination (plus a subscription to Claude, ten minutes of setup and 30 minutes of playing!). Claude will hold your hand through every single step. Just ask if you don't understand. Within 30 minutes you'll have something working and your perspective will have changed. There's a difference between knowing what's possible and doing it yourself. Go do it. #### Prompt: Your first build ``` I've never built anything with code before. I work in [your field]. I want to build [describe the thing you wish existed: a website, a tool, an automation, a dashboard]. Walk me through every single step from scratch. Assume I know nothing about terminals, code, or deployment. Hold my hand. If I don't understand something, I'll ask. Start with what I need to install and set up. ``` ### Measure outcomes, not logins *Leadership & adoption* Accenture now tracks weekly AI tool logins for senior staff and links them to promotion decisions. The internal reaction? Staff call the tools "broken slop generators." Measuring whether people opened the app tells you nothing about whether the work got better. If you're going to track AI adoption, track the outcomes: did quality improve? Did turnaround times fall? Did clients notice? Log-in counts measure compliance. It's easy but lazy. Measure capability, instead. #### Prompt: Design an outcome-based measurement framework ``` My organisation currently tracks AI adoption by [how you measure it, e.g. licence usage, login counts, tool access rates]. I want to measure whether AI is actually making our work better, not just whether people are opening the app. My team does [describe the work]. Propose 5 outcome metrics I could track monthly, each with: 1. What to measure 2. How to measure it (data source, method) 3. A one-sentence rationale for why it's better than a login count 4. What "good" looks like after 3 months Keep it practical. I need things I can start measuring this week, not a six-month data infrastructure project. ```