Prompt Kit
Prompt Kit: You're Prompting Like It's Last Month
Most prompt kits give you a handful of clever prompts and call it a day. This one is different.
The article laid out four disciplines. Not tips. Not hacks. Disciplines, the kind that compound over months. This kit turns those disciplines into artifacts you can build today: your personal context document, your first real specification, your delegation framework, and your eval harness.
But first, there's something most prompt kits skip entirely. And it might be the most important part.
How to use this kit
Start here, every time. Prompt 0 is not a prompt. It's a thinking exercise you do with pen and paper, a whiteboard, or a voice memo before you open any AI session. Takes 10 minutes. It's the reason the rest of the kit works. Skip it and you'll build on the AI's version of what you wanted instead of your own. I cannot stress this enough.
If you have 10 more minutes after that, jump to the ⚡ 10-Minute Quick Start section. Two prompts: a rapid diagnostic that identifies your biggest gap across the four disciplines, and a fast problem statement rewriter that builds the core primitive Tobi Lütke identified. Highest leverage per minute in this entire kit.
For the full build-out, work through the complete kit in order. Each prompt produces a standalone artifact, but they build on each other the way the article describes. Prompt craft foundations support context engineering, which supports intent engineering, which supports specification engineering. Run them in any thinking-capable model (ChatGPT, Claude, Gemini). The Specification Engineer and Intent Framework Builder benefit especially from models with strong structured-output capabilities.
One session or many. Each prompt is independent. You can run one today and another next week. The Eval Harness Builder is designed to be revisited periodically, especially after model updates.
🧠 Prompt 0: The Human Prompt
This is not a prompt. It's the most important exercise in this entire kit.
Every other prompt in this kit produces better output when you do this first. Skip it and you'll build on the AI's version of what you wanted instead of your own. Ten minutes. That's all it takes.
Here's the deal: Write down or screenshot the seven questions below, then close the laptop. Grab a pen, a whiteboard, a napkin, a voice memo app — anything that doesn't talk back. Work through the questions away from a screen. The whole point is to get your thinking out of your head before AI has a chance to reshape it.
Seriously. Write down the questions. Step away. Come back when you've got answers.
The Human Prompt
Job: Gets your thinking out of your head and onto paper before you open an AI session, so you show up driving the conversation instead of reacting to whatever the AI gives you.
When to use: Before any significant AI task. Before running any prompt in this kit. Before delegating anything to an agent. This is the pre-flight check. Every single time.
What you'll get: A one-page brain dump (written by hand, spoken out loud, or scrawled on a whiteboard) that captures what you're actually trying to accomplish, what good looks like, and where the hard parts are. This isn't an artifact you paste into AI. It's the clarity you bring to AI.
Tools: Pen and paper. A whiteboard. A voice memo app. A blank notebook. Anything that doesn't talk back.
Why This Exists
Here's the thing nobody tells you about working with AI early in your thinking process.
It's too fluent.
You say something half-formed. The AI hands you back a polished, confident version. And suddenly you're evaluating its framing instead of finishing your own. You cave. You adopt its structure. You lose the thread of what you actually wanted because its version sounded smarter.
It wasn't.
The pattern is consistent. The times AI's version beat mine, I'd already done the thinking. I could evaluate what it gave me against what I knew I wanted. The times it led me astray, I hadn't. I showed up without a clear picture and let a language model fill in the blanks.
The problem is that AI fills blanks with statistical plausibility. That's a polite way of saying it guesses confidently. And confident guesses are very easy to mistake for good ideas.
Here's an analogy. If you walk into a meeting without knowing what you want, the most articulate person in the room decides for you. AI is the most articulate thing you've ever talked to. It will never stumble over a word. It will never pause to collect its thoughts. It will never say "I'm not sure, let me think about that." It just talks. Fluently. Confidently. And if you haven't figured out what you think first, you'll end up thinking what it thinks.
Get clear before you open the chat window.
Pen and paper don't have opinions. A whiteboard doesn't autocomplete your strategy. A voice memo doesn't rephrase your intent. Those are features, not limitations.
✍️ The Seven Questions
Write these down. Step away from the screen. Work through them in order.
This takes 5 to 15 minutes. Do it in whatever medium feels natural. Write it, say it out loud, draw it on a napkin. The structure matters. The format doesn't.
1. What am I actually trying to accomplish?
Not the task. The outcome. "Write a blog post" is a task. "Convince mid-level managers that their AI strategy has a blind spot they haven't considered" is an outcome. Say it in one sentence. If you can't get it to one sentence, you don't know what you want yet. That's fine. Keep talking it through until you do.
2. Why does this matter?
What happens if this goes well? What happens if you don't do it at all? This forces you to separate the things that actually need to be good from the things that just need to exist. Not everything is high-stakes. Knowing which category you're in changes how much specification work the task actually requires.
3. What does "done" look like?
Describe the finished thing. Not the process. The output. If someone handed it to you completed, what would make you say "yes, that's it"? Be specific. Length, format, tone, level of detail, who it's for, what they should feel or do or know after they encounter it.
Let me be concrete: if you can't describe what done looks like, you're not ready to delegate this to anyone. Human or AI.
4. What does "wrong" look like?
This is the one people skip. It's the most important.
What would make you look at the output and say "no, that's not what I meant," even if it's polished and technically correct? What's the subtle failure mode? Think about the last time AI (or a person) delivered something that checked every box but still missed the point. What did they miss?
That's the constraint you need to encode. Write it down now, while you can see it, before the AI's confident framing makes you forget you ever had a different vision.
5. What do I already know about this that I haven't written down?
The institutional knowledge. The context. The unwritten rules. The thing that's obvious to you but wouldn't be obvious to someone encountering this task for the first time.
This is the stuff that lives in your head and evaporates the second you let someone else start working without it. Say it out loud or write it down. All of it.
6. What are the pieces?
Break it down. What are the components, subtasks, chunks? What comes first? What depends on what? What could be done independently?
You're building the decomposition that makes a specification work. But you're doing it in your own head first, where you can see the whole picture and catch the dependencies that a task list would miss.
7. What's the hard part?
Every task has one piece that's genuinely difficult and several pieces that are just effort. Name the hard part. Where are the judgment calls? Where could this go sideways? Where are you least certain?
This is where your specification needs the most detail. And it's the part most people gloss over because it's uncomfortable to sit with uncertainty.
What to Do With Your Answers
You now have a brain dump that represents your thinking. Uncontaminated by AI's framing. Unpolished by someone else's fluency.
It's messy. Good. The mess is yours.
When you open an AI session — whether it's running a prompt from this kit or starting a fresh task — you're not showing up empty-handed and hoping the AI asks the right questions. You're showing up with answers. Your definition of done. Your failure modes. Your decomposition. Your hard parts. You load that context deliberately. And you evaluate AI output against your criteria, not against the AI's confident-sounding version of criteria you never actually agreed to.
The whole argument is that the bottleneck moved from "talking to AI well" to "knowing what you want before AI starts working." This exercise is how you build that muscle.
Ten minutes. Pen and paper. No AI involved.
Do this before running any of the prompts below. It changes everything.
⚡ 10-Minute Quick Start
Prompt Q1: Rapid Four-Discipline Diagnostic + Starter Context Doc
Job: Identifies your biggest skill gap across the four disciplines and produces a usable personal context document in a single fast session.
When to use: Right now. This is your starting point — it tells you where to focus and gives you an artifact that immediately improves every future AI session.
What you'll get: A scored assessment across all four disciplines, your #1 priority gap, and a starter personal context document you can paste into future sessions.
What the AI will ask you: Your role, how you currently use AI, a few examples of recent AI tasks, whether you manage people or systems.
Prompt Q2: Self-Contained Problem Statement Rewriter
Job: Takes your typical vague, conversational AI requests and rewrites them as fully self-contained problem statements — the core primitive that Tobi Lütke identified as the fundamental skill.
When to use: When you want to practice the #1 new primitive quickly. Paste in a request you'd normally type into a chat window, and see what a self-contained version looks like.
What you'll get: Your original request transformed into a complete, self-contained problem statement, plus an annotation showing every gap in context your original had.
What the AI will ask you: For one or more examples of requests you'd typically make to AI, plus a few clarifying questions to fill in the missing context.
Complete Kit
Prompt 1: Four-Discipline Deep Diagnostic
Job: Conducts a thorough assessment of your current AI skills across all four disciplines and produces a personalized 4-month development roadmap aligned to the article's progression.
When to use: When you want a comprehensive audit — not the quick version — with a real action plan. Best done once, then revisited quarterly.
What you'll get: A detailed scorecard, gap analysis, and a month-by-month roadmap tailored to your role, with specific exercises for each phase.
What the AI will ask you: Detailed questions about your role, AI usage patterns, delegation practices, whether you've built reusable AI infrastructure, and your organizational context.
Prompt 2: Personal Context Document Builder
Job: Produces a comprehensive personal context document — your "CLAUDE.md for everything" — through a structured deep interview about your work, standards, and institutional knowledge.
When to use: Month 2 of the roadmap. This is the single artifact that most immediately improves AI output quality across every session. Build it once, update it monthly.
What you'll get: A complete, formatted context document you paste at the start of AI sessions. Covers role, goals, quality standards, communication preferences, institutional knowledge, constraints, and known AI interaction patterns.
What the AI will ask you: Deep questions about your role, organization, standards, preferences, institutional knowledge, and how you evaluate quality.
Prompt 3: Specification Engineer
Job: Collaboratively builds a complete specification document for a real project — the kind of document an autonomous agent can execute against over hours or days without human intervention.
When to use: When you have a real project (not a toy problem) and want to practice the discipline of specification engineering. This is the Month 3 exercise from the article's roadmap.
What you'll get: A structured SPEC.md-style document with acceptance criteria, constraint architecture, task decomposition, evaluation criteria, and a clear definition of done.
What the AI will ask you: Deep questions about the project — not obvious ones, but edge cases, tradeoffs, failure modes, and the hard parts you might not have considered.
Prompt 4: Intent & Delegation Framework Builder
Job: Extracts the implicit decision-making rules your team operates by and encodes them into a structured framework that both AI agents and human team members can act on.
When to use: Month 4 of the roadmap, or anytime you're deploying AI agents that need to make judgment calls. Critical if you've experienced the "technically correct but wrong" problem the article describes (the Klarna pattern).
What you'll get: A structured delegation framework covering decision authorities, tradeoff hierarchies, escalation triggers, and quality thresholds — formatted for both human reference and AI agent consumption.
What the AI will ask you: About the types of decisions your team faces, how tradeoffs get resolved, what gets escalated, and what "good judgment" looks like in your context.
Prompt 5: Eval Harness Builder
Job: Creates a personal evaluation suite — the Lütke pattern — for your recurring AI tasks, so you can systematically test quality and catch regressions across model updates.
When to use: Month 1 of the roadmap (start immediately), then revisit after every major model release. This is how you build intuition for where models fail on your specific work.
What you'll get: A set of 5-7 test cases with inputs, expected outputs, and quality criteria you can run against any model to benchmark performance on your actual tasks.
What the AI will ask you: About your most frequent AI tasks, what "good" looks like for each, and examples of outputs you've been satisfied and dissatisfied with.
Prompt 6: Constraint Architecture Designer
Job: Takes a task you're about to delegate and systematically identifies the constraint architecture — musts, must-nots, preferences, and escalation triggers — that prevents the smart-but-wrong failure mode.
When to use: Before delegating any significant task to an AI agent. This is the practice exercise for the article's third primitive — the habit that prevents the "80% problem."
What you'll get: A four-quadrant constraint document for your specific task, plus the failure modes it prevents.
What the AI will ask you: About the task you're delegating, and specifically about what could go wrong — the subtle ways a competent executor could satisfy the request but produce the wrong outcome.