16 articles
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Start with the boring chore
Anyone who wants to automate usually thinks of the biggest process right away. I would start with the small tasks that you do the exact same way every time.
When we get to building skills in a training session, I always ask what people would like to automate. And then I almost always get the same kind of answer. Someone names the biggest process in their job. The work they have dreamed of for years that would one day happen by itself. Usually, that is also the work that involves the most thinking.
I understand that. Yet I always say the same thing: put that out of your mind for a moment. You do not start there. Look at the low hanging fruit first, the little things you do again every week.
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Tell your agent when it is finished
An agent keeps working until it is finished, if you tell it what finished means. This is how I describe in advance what a good result looks like, as a checklist.
There comes a moment in my training sessions when the foundation is ready. The folders are set up, the first skills are built. And then I ask a new question: what do you want your agent to do while you go and eat? Then it suddenly becomes about something else than asking a question and getting an answer. It is about an agent that continues working on its own.
There is a word for that, loop engineering. You are going to hear that a lot in the coming period. It sounds technical. But the idea behind it is simple, and I use it very often myself.
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You do not have to write out your skill yourself
Building a skill sounds like a lot of writing. Today I leave that to my AI. Four ways to let your AI create a skill, including by demonstrating it.
When I started with skills, I wrote them out completely myself. Every step, every exception, in a Markdown file. I actually do not do that anymore. You just ask ChatGPT, Claude or Copilot to write a skill, and it knows how to do that. A folder with a lowercase name, a SKILL.md file in it, the right description at the top. Your AI handles that itself.
What you still do yourself is tell your AI what the skill has to do. And there are different ways to do that. This week I put them side by side in a training, because many people think there is only one.
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Why I do not tie myself to one AI provider
The big AI companies are bringing you in cheaply right now. I prefer to spread my work across multiple tools, and I explain why and how you can do that yourself.
In a training about Copilot this week, we talked about licences. Microsoft is changing a few things again, and new features are being added. I also gave my own opinion then, because there is a question behind it that many companies are not asking today. What happens if you put all your work with one AI provider?
I do not tie myself to that. And that has little to do with Microsoft alone. It applies to all the big players.
At the bottom of this article is a prompt you can copy to see where you are dependent.
Who is paying today
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What are you still paying for every month?
Many small costs keep running because no one looks at them. Which tasks you can take into your own hands with AI, and how you start small without stacking subscriptions.
At the end of the month you sit with your bank statements. A subscription here, an invoice from the freelancer there, the virtual assistant, the little tool for transcribing conversations, another tool to schedule your social media. Each amount on its own is not too bad. Together they form a list you would rather not look at for too long.
Ask yourself honestly which of those things you pay for because it is truly a profession that someone else can do better, and which ones because you simply never got around to doing them yourself.
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Babysit your AI, even if it can do it itself
You can give an agent a goal and let it go, or watch along step by step. I teach the second in my workshops, and I explain why.
There are two ways to build something with AI. In the beginning they actually look the same. With the first you write a good, detailed description of what you want. You give your agent a goal, and then you let it go. With the second you work step by step. After each step you look at what it has done. You try to understand it, and then you adjust.
In my workshops I teach the second. The models can do a lot, and they get better every month. But I have seen what happens when no one watches along anymore.
Below this article you will find the share card with the babysitting loop, which is free for Members.
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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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Fewer agents, more skills
I used to build a separate agent for every task, until I no longer knew who did what. Now I work with a few agents that share skills.
In a workshop about agents I got a question that took me a long time to figure out myself. One agent can have multiple skills. Why not just build multiple agents that can each do one thing well? That seemed a lot clearer to that person.
That is actually how we did it too, about eight months ago. We built a separate agent for every task. One for translations, one for newsletters, one for quotes, one for reports. Each with its own instructions and its own conversations. That felt very organised in the beginning.
At the bottom of this article is a prompt that helps you tidy up your own agents, which you can just copy.
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Model, app, environment: know which layer you are in
ChatGPT, Claude, VS Code, Cursor, Craft Agents: beginners often lump them all together. I work with three layers, and they help you choose.
At the end of a long workshop day, I asked the group what they took away from it. And one participant said something I have not forgotten since. He had used two programmes side by side all day. And only now did he realise they were two different things. Another participant did not see the added value of an agent app, because 'I already have ChatGPT'.
Those are actually questions everyone has. Only not everyone dares to ask them. And they all have the same cause. We talk about AI tools as if they are one thing. But actually there are three, slotted neatly into each other.
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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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Create your skill at the end of the chat
Not every task needs a skill. I would create one for work you do often, and only at the end of a good chat.
Should I create a skill to choose a new washing machine? I get that question in some shape or form in almost every workshop. Since everyone knows what skills are, it keeps coming back. And my counter question is always the same: how many washing machines are you going to buy? Maybe one every ten years, if things go well. So I would not create a skill for that.
For the tasks where it does pay off, you will find the question I ask at the bottom of this article.
The washing machine test
A skill is a fixed method that your AI can call upon. It contains the steps, the quality criteria, the tone and the format. You write it once. And after that, your AI follows the same path every time. That is where its value lies, and also its limit.
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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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Choose the smallest model that can handle the work
The smartest model is not always the best. Why you consciously choose which model to use for each task, and what you gain in control, time and costs.
You do not need to take a sports car to get bread from the bakery. Yet that is exactly what most people do with AI. They turn on the most powerful model their subscription allows, and use it for everything. A four-line poem, rewriting an email, cleaning up a folder full of files. All on the most expensive engine.
At the bottom of this article you will find a prompt that helps you choose the appropriate model for each task.
More power is not always better
It feels logical: the smartest model gives the best result. For complex thinking that is also true. But for many daily tasks it works counterproductively. The largest models have a tendency to do too much. They write longer than necessary, add things you did not ask for, and sometimes run ahead of you in a direction you did not want.
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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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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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