22 articles
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The mountain of emails on Monday morning
The manual work that returns every week does not have to stay with you. What AI can take over from your emails, reports and quotes, and how you start small.
Sunday evening. The laptop is open on the kitchen table and you should actually be sitting on the sofa by now. But quotes still need to go out, three clients are waiting for last week's report and your inbox is full of requests that you have to answer all just a little bit differently. It is not difficult work. It is work that returns every week, and that steals exactly the hours you wanted to give to your clients.
This is what it looks like for many freelancers I meet. They are good at their profession, and in the evening they are stuck with the manual work around it.
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Shareable card, the babysitting loop for vibe coding
One card to hang next to your screen or share with your team, plus six sentences to guide your AI in plain language while it builds for you.
In my building training sessions, one sentence comes back so often that participants finish it themselves after an hour. Dumb it down, chunk it down, and babysit your AI. It fits on a card, and you can find that card below. Hang it next to your screen, put it in your team channel, or send it to that colleague who just told their agent to 'solve it yourself'.
The card
You are free to share it, as long as the name stays on it.
Frequently asked questions
What does dumb it down, chunk it down and babysit your AI mean?
Dumb it down is saying it in plain language, just like to a smart colleague who does not know your project. Chunk it down is cutting your project into small, checkable pieces. Babysit is going to look if it is correct after every step before you ask for the next one.
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Swipe file, ten sentences to stop your AI losing the plot
Ten sentences to copy for the start, the middle and the end of a long AI task, so you work with a handover instead of a summary of a summary.
A long task with AI rarely goes wrong in a single moment. It slips away. An agreement about the tone disappears, an exception is forgotten, and three sessions later your AI is working on a project that is just not yours anymore. With the right sentence at the right time you stop that. These are the ten I use myself, ordered by the moment you need them.
How you use this
A swipe file is a cheat sheet. You do not read it from front to back, you grab the sentence you need right now. Save this page to your favourites, or paste the sentences into a note next to your AI tool.
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Give your AI a small brain, not a library
Anyone who puts all their documents into a knowledge base for AI will see their assistant drown in it. A short index that points the way actually works much better.
Last week in a workshop about agents I showed something I normally keep for another training course. I showed the second brain that my agents use. A participant immediately asked what the difference is with a knowledge base where you put all your knowledge. I thought that was a good question. I actually know companies that are doing exactly that. They put their entire knowledge management into one large knowledge base, so that AI can access it everywhere. That is a logical thought. And it is also the mistake I made myself.
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Brief your AI with TOUCH instead of a SuperPrompt
I now brief my AI with five letters: task, outcome, open questions, context and handover, because today's models guess the rest themselves.
In a workshop last week I built an onboarding assistant for an HR department live. I did not write a half-page prompt for that. It also contained no role and no list of tone instructions. It was five short steps. And I did not upload a single document for it.
Copy the prompt at the bottom of this article, then you have those five steps ready for your own task right away.
Why the SuperPrompt has done its job
I have worked long and often with an extensive prompt framework myself, CRAFTER. With the older models you had to write everything out. They did literally what it said. And they filled in the rest themselves, often incorrectly.
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Ask your AI for lessons, not excuses
When my AI made a mistake, it started defending itself. I asked it for a lesson, and that is now in its memory.
This summer I came back from a trip. And the Monday after I had to give a training. During my holiday I had not really followed my emails. Had the confirmation come in or not? So I asked my AI assistant. It went looking and said it had found nothing. I was already standing at the client's door by then. And I really started to sweat.
Feel free to copy the prompt at the bottom of this article, you can use it the next time your AI misses something.
The same question to a second assistant
I thought to myself that this could not be right. So I asked exactly the same question to a second assistant, a different model. It found the email within seconds. It was just sitting there.
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Give your AI a few custom shortcuts
Two colons and a short word, and your AI knows what you mean. This is how I put custom commands in my custom instructions, for what I ask every week.
In a workshop this week I showed my custom instructions on the screen. They state that I do not want to see emoji. And not too many bullet points, and normal capital letters. And below that is a list of lines that start with two colons. Those are the rules I use most often, and they are barely a word long.
Feel free to copy the prompt at the bottom of this article, then you can write your first shortcut together with your AI.
What you type again every week
Have a look at your own chat history. Chances are you formulate the same questions over and over again. Translate this to Dutch. Write a short bio about me. Make this text a bit looser. Every time you type a sentence, sometimes two. And every time it is slightly different, so the result is too.
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Get your AI out of the browser
An AI in a browser window can talk. An AI on your own computer can work: read, organise and edit files. Why this step makes the difference, and how you start safely.
You actually become much more of a manager of work than an executor of work. I said that in a workshop, while we looked together at what happens when you give the AI access to one folder on your laptop. A messy downloads folder is tidied up, separate Excel files are merged into one overview, and a first version of a follow-up email is ready.
The same AI, but no longer in the browser.
If you want to try it yourself, copy the prompt at the bottom of this article and give it to your desktop app.
Talking or working
Most people know AI as a chat window in the browser. You ask a question, you get text back, and you copy what you need to your own documents. That is useful, but it remains talking. Everything that needs to happen with your files after that, you do yourself.
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The most important file in your AI project is not code
Anyone building with an AI agent notices they have to explain where everything is over and over again. One simple Markdown file solves that. What belongs in it, and why.
When I open a project in a building training, participants usually look at the code first. I then point to another file. A plain text file, a few screens long, without a single line of code. That is the file that determines whether your agent can work independently or comes to ask you something every five minutes.
At the bottom of this article is a prompt with which your agent helps you to draft such a file.
The problem everyone knows
Anyone who works on a project with an AI agent for a while knows it. Every new session starts with an explanation. This is the project, this is how it is put together, the files are there, this is how we want it. If you forget something, the agent goes looking for itself, and makes assumptions that you have to correct later.
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Before you build an agent, you draw the process
Can AI do my reservations? The honest answer starts with a drawing of the process. Why process thinking is the skill that determines whether an agent works.
Someone I know started a B&B and asked me if AI could do the reservations. A simple question, with an answer I like to use in workshops. Because the short answer is yes. The honest answer starts with a drawing.
At the bottom of this article you will find a prompt that lets AI look at your process drawing, which you can just copy.
What is really behind a reservation
When we drew it out together, a lot came up. Bookings come in via a booking platform, via email, via text message and even via comments under posts on social media. Each channel requires its own access, its own settings and its own login details. An agent cannot reply to a message it does not see.
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What Copilot can and cannot see
Sometimes Copilot seems to know everything, sometimes it cannot find a document that is right there. How its index works, what falls out of view, and what you can do about it.
The same thing happens in almost every Copilot workshop. Someone asks a question about a project and gets a surprisingly complete answer, with references to emails, a Teams conversation and a document on SharePoint. A few minutes later, a colleague asks about a procedure that has existed for years, and Copilot knows nothing about it. How is that possible?
At the bottom of this article is a prompt you can use to test for yourself which of your documents Copilot finds.
A signpost, not an archive
The answer lies in how Copilot gets its knowledge. Within a Microsoft 365 environment, Copilot builds an index across your OneDrive, SharePoint, mailbox and Teams. Microsoft currently calls this layer Work IQ, and the name might still change.
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Three types of agents, and why the middle one does the work
Knowledge agents, work agents and autonomous agents. Why the autonomous dream often derails today, and where the real gain is for those starting now.
The word agent pops up in every presentation about AI these days. Everyone has one, everyone is building one, and everyone means something different by it. That is why I first make a simple distinction in my workshops. There are three types of agents, and they differ mainly in how much they do independently.
At the bottom of this article you will find a prompt that helps you describe your first work agent.
From answering to acting independently
I call the first type the knowledge agent. It only responds when you ask something, in a chat window. You give it instructions and sources, for example your brand style or your product information, and it answers within that framework. Handy, but it does nothing on its own.
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First your backlog, then your delegation list, then something new
Anyone who works well with agents goes through three phases. The first two are about efficiency. The third is about work you could never have done before.
When I speak to people who have been working well with AI agents for a while, I always hear the same story. It happens in three phases. And the interesting thing is that almost no one sees the third phase coming.
Feel free to copy the prompt at the bottom of this article, so you can look for your own third phase together with your AI.
Phase one: the backlog
First you tidy up. All those tasks that have been on your list for months and that you never got around to. The folders you always wanted to organise. The quotes you still had to follow up on. The overview of your clients that has been out of date for two years.
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Memory or instruction? Facts and behaviour belong apart
Why your AI keeps forgetting your house style, and how you put facts, behavioural rules and project knowledge each in their own place so it works consistently every time.
'Why does it always forget my tone of voice?' I get that question in almost every workshop. Usually it turns out that everything is mixed together in one large instruction field: who you are, who you work for, how you write, which clients you have, and what formatting you want. One long piece of text that is sent along with every conversation and that the model can only half follow.
At the bottom of this article you will find a prompt that splits your instructions for you, which you can just copy.
Two types of information
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Markdown is the native language of AI
Why a PDF burdens your AI more than the same text in Markdown, and two small habits that instantly improve your output and your instructions.
When I ask in a workshop who has ever dragged a PDF into an AI chat, almost all hands go up. When I ask who knows what Markdown is, only a few remain. Yet that second one is probably the most underestimated piece of knowledge in working with AI.
At the bottom of this article you will find a prompt that lets you convert a document to Markdown yourself, which you can copy just like that.
What Markdown is
Markdown is plain text with a few simple characters for structure. A hash for a title. Two asterisks around a word for bold. A hyphen for a list. It is nothing more than that. You can read it in any text editor, and it still looks logical even without formatting.
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Edit your question instead of chatting on
If the answer is not right, correcting it in a new message is the reflex. Editing your first question often works better, and your AI stays sharper.
You ask AI a question, the answer is not quite right, and you type: no, shorter. Then: no, more formal. Then: and without that list. Five messages later you have something usable, and a conversation full of half corrections. I see it in every workshop. It is the most natural reflex, and often not the best one.
At the bottom of this article you will find a prompt that helps you see what was missing in your question.
What happens when you keep correcting
Every correction you type in a new message is added to everything that was already there. Your original question, the failed first answer, your first correction, the second answer, your second correction. The model has to take all those layers with it and deduce for itself what you actually want now.
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Write a handover instead of compressing
A summary of a summary loses something every time. With a handover document and a phased plan, you work on large tasks without your AI losing the thread.
Many AI tools nowadays have a button that shortens your long conversation. You click it, the chat is summarised, and you can continue. It feels efficient. In my workshops, I still advise against it for anything that matters. There is a better way, and it only costs you one extra step.
Below this article, you will also find the swipe file with sentences for a good handover, free if you have an account.
What compressing does to your conversation
Compressing, or compacting as some tools call it, summarises your conversation and throws the rest away. The first time, you notice little difference. But with a long task, it happens again, and again. After a few rounds, you are working with a summary of a summary of a summary.
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Skills or flows: when plain language is the wrong format
Describing a work process in plain language for an agent is flexible, but not always smart. When it is better to build a fixed workflow, and how to spot the difference.
One of the most beautiful things about working with AI agents is that you can describe processes in plain language. You write down how you do something, step by step, and the agent follows it. Such a description is called a skill in many tools. But in my workshops I also warn people: not every process belongs in plain language.
At the bottom of this article is a prompt that helps you figure that out for your own process.
What a skill is
A skill is essentially a description of a work process. A standard operating procedure, written in Markdown, that an agent reads and executes. For example: if someone wants an appointment, ask for their name, date and location, check the calendar to see if it fits, suggest an alternative if it does not fit, and send the invitation after agreement.
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Vibe coding does not start with the code
Anyone building an app or tool with AI gets stuck by starting to build immediately. Why the discovery phase and a few early choices save you days of work later.
Vibe coding is really just talking to your computer, and it builds it. That is how I explain it to participants who have never heard the term. You describe what you want in plain language, and an AI agent writes the code. A tool, a website, a small app. What used to require a team, one person can do today.
But anyone starting for the first time almost always runs into the same thing. After a promising start, things go wrong somewhere, and the AI no longer seems able to change direction.
Copy the prompt at the bottom of this article, with it you brainstorm about your idea first before you start building.
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Why your longest chat is your worst chat
A long chat does not train your AI, it fills the context window. Why the quality drops halfway through, and the habit that solves it.
In almost every workshop someone proudly tells me they have been working in the same chat for weeks. 'That way it gets to know me.' I understand the thought. It feels like a colleague who increasingly knows how you work. Except it is not true, and precisely because of that the answer gets worse instead of better after a while.
At the bottom of this article is the prompt you use to wrap up a long chat, so you can start fresh properly.
A chat is not a learning process
A language model does not learn while you talk to it. It predicts the next word over and over again, based on what is in its context window. That context window is its working memory: your question, the files you added, and the entire conversation up to now.
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The CRAFTER SuperPrompt Framework, step by step
Seven questions you answer before you press enter: this is how you turn a vague prompt into a reliable briefing, for older and simpler AI models.
The CRAFTER framework is a structured way to brief AI, turning vague prompts into reliable, repeatable results. It is the difference between getting lucky once and getting consistent quality every time.
At the bottom of this article you will find a prompt that makes AI question you first before it starts your task.
Good to know. CRAFTER is a valid framework for older and simpler AI models. For ChatGPT 5.x and Claude 5.x it no longer applies.
Why a fixed structure?
The professionals who truly get value out of AI are not the ones with the most tools. They are the ones with a system. CRAFTER is that system for a single prompt: seven questions you answer before you press enter. Context, Role, Action, Format, Target audience, Examples and Refining.
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From chatbot to workflow: building your first AI workflow
The difference between a chatbot and an AI agent, and an exercise to build your first own AI workflow this week.
There is a shift happening that most people have not noticed yet. We are moving from asking AI questions to managing AI workflows.
At the bottom of this article is a prompt that helps you map out your first workflow together with AI.
What is the difference between AI agents and chatbots?
A chatbot waits for your input and gives you an answer. An AI agent gets a goal, breaks it down into steps, uses tools and delivers a result. The difference is like the difference between asking someone for directions and hiring a driver who knows the route.
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