What People Who Use AI Well Have in Common: They’re Obsessed with Keeping Records

What follows includes actual excerpts from my own CLAUDE.md.

It isn’t that they write dazzling prompts. It isn’t that they’re the first to jump ship to every new AI tool.

They keep records.

They record what they like and what they don’t. They leave behind the decisions they made at work and the reasons for them. When AI produces something off the mark, they don’t just ask again and move on.

They write down what they didn’t like, why it felt wrong, and what to do differently next time.

To exaggerate a little:

the people who use AI well are people obsessed with keeping records.

Not long ago, I went back and reread part of

CLAUDE.md

— the file I actually use.

There were no grand prompts inside.

When writing Korean web copy, avoid translationese and awkward Konglish. When addressing business owners, say “대표님” rather than “사장님.” When expressing prices, prefer “정가” over “원가” depending on the situation. Write clear, trustworthy sentences fit for a B2B service.

I had also marked the distinction between loanwords that must be kept, like brand and product names, and expressions that sound more natural rendered in Korean.

The reason I started keeping records like this is simple.

There is no way AI could know my taste from the start.

I hate translationese.

Expressions that read like English sentences transplanted straight into Korean, awkward words nobody would actually use, English mixed in by force to look sophisticated — they all make me wince.

One day, looking at copy the AI had produced, I wrote this down myself:

“Sentences like this make me want to tear the page apart.”

Harsh wording — but that is how clear the taste was.

But unless I say it, AI cannot know.

AI cannot see my face. It cannot tell that I frown while reading a sentence. It does not know why I quietly delete one word and replace it with another.

AI does not learn me by reading the room.

It learns me through records.

Many people search for the “perfect prompt” to use AI well.

“Ask it this way and you get good answers.” “Put this sentence in front and it answers like an expert.” “Assign it a role and make it think step by step.”

Of course, how you ask matters.

But work with AI long enough on real tasks, and you begin to see something that matters more than one brilliant question.

Accumulated context.

At first, the AI writes sentences I hate. I ask it to fix them.

The second time, it uses similar expressions. I explain again why they don’t sit right.

The third time, it gets a little better. That is when I tell it why it improved.

If this process ends inside the chat window, the next task starts from the beginning all over again.

Why I made those edits last time, which expressions I prefer, by what standard I judge a sentence — all of it has to be explained again.

People who keep records do not let this repetition stand.

“I’m going to need this again.”

At that moment, the feedback inside the conversation moves into a document.

From then on, they don’t introduce themselves to the AI anew each time.

They hand it a manual on how to work with them.

In the AI era, most people use similar tools.

The same ChatGPT, the same Claude, similar features.

There is no special AI secretly served to a chosen few. Most people use similar models that anyone can access.

Yet the results differ enormously.

Why?

Because the difference comes less from which AI you use than from what context you give it.

The sentence structures I prefer. The brand principles that must never bend. The way I look at customers. The words I use to describe the product. Past decisions and their reasons. Approaches that failed. Output that was good and output that wasn’t.

As this information accumulates, the AI begins to produce not generic answers but answers fitted to me.

In the end, personalizing AI is not a matter of switching on some special feature.

It is a matter of recording enough about yourself.

A person without records questions the AI like someone meeting a stranger for the first time, every time.

A person with records makes requests the way you hand work to a colleague of many years.

Even with the same AI, the results cannot help but differ.

When people hear “records,” they usually think of diaries or meeting minutes.

Something you keep in order to read again later.

But records in the AI era are a little different.

You don’t keep them only to read them.

You keep them to use again in the next piece of work.

Say the AI produces copy you don’t like.

Most people say:

“Make it a bit more natural.”

The result may improve for the moment. But what “natural” precisely means is never captured.

People who record go one level deeper.

Which part felt unnatural? Why did it read like translationese? Which word swap made it better? Can this criterion be applied to other pages too?

And they set it down as a rule.

A record kept this way does not end with a single edit.

It gets used on the landing page. On the pricing page. In customer notices and emails. Even if you switch to a different AI tool, it carries over intact.

Records turn a one-time experience into an asset that can be used again and again.

Using AI, you sometimes feel a strange fatigue.

“I said this before.” “Why is it using the same expression again?” “It understood perfectly last time.”

But from the AI’s side, this may be only natural.

The standards that live only in our heads have never been delivered.

Even if you said it once in a previous conversation, that context may not fully carry into a new one.

The problem does not always lie in the AI’s memory.

Sometimes it lies in how we manage our knowledge.

It is the same when working with people.

Principles that exist only in the founder’s head. Standards that only the longest-serving teammate knows. Tastes never once explained in words, which everyone obeys by intuition.

When the organization is small, this way of working holds.

But as people multiply, projects multiply, and AI joins the work, problems appear.

Some people know and some don’t. The same mistakes repeat. The same explanations have to be given again and again.

AI throws this problem into sharper relief.

A standard that is not written down is the same as one that does not exist.

So once you start using AI properly, documents naturally multiply.

Project briefs. Brand language guides. Task checklists. Mistakes that keep happening. Examples of good and bad output. The judgment principles you hold most important.

These documents look like they exist for the AI, but in the end they help people too.

A principle made clear enough to explain to an AI can be explained to a teammate.

Records begun in order to use AI well end up improving how the whole organization works.

Most people have taste.

This sentence is good. This design falls flat. This expression is tacky. This deliverable somehow doesn’t inspire trust.

The problem is that they cannot explain why.

“It just feels that way.”

In that state, taste remains a private sensation.

It cannot be passed to another person, cannot be taught to an AI, cannot be reused in the next task.

Recording forces that sensation into language.

Why is it good? Why is it bad? What is different? Under what conditions are exceptions allowed?

Through this process, vague taste becomes a criterion of judgment.

And when criteria accumulate, they become a system.

Records turn taste into reproducible skill.

By this point, some people may feel a weight before they even start.

As if they need to build a Notion database, reorganize their folder tree, construct the perfect knowledge management system.

There is no need.

One document is enough.

The file name doesn’t matter either.

CLAUDE.md

AI_WORKING_GUIDE.md

How to work with me.md

Things to tell the AI.md

Anything is fine.

Just write these kinds of things into it, bit by bit.

It doesn’t need to be perfectly organized from the start.

When you catch yourself explaining the same thing to the AI for the second time, record it.

That sentence becomes a new rule.

“Do not use this expression.” “In this case, prefer this word.” “Speak to customers in this tone.” “When the output looks like this, check this first.”

Adding even one line a day is enough.

A few months later, that document is no longer a simple memo but a working manual that understands you remarkably well.

The ability to use AI well is not only the ability to talk to it well.

It is the ability to structure your own experience. The ability to put taste into language. The ability to leave your criteria outside your head. The ability to reuse what you learned once in the next piece of work.

For those who record, the AI molds itself to them the longer they work.

Today’s feedback becomes tomorrow’s default. Today’s mistake becomes the next project’s checklist. Today’s decision becomes the criterion from here on.

Those who don’t record, by contrast, start every job by explaining everything from the beginning again.

They find the same mistakes, give the same feedback, fix the same sentences again.

Time passes, but no system remains.

What matters in the AI era is not who gets one better answer, once.

It is who keeps accumulating their experience and judgment so that the next answer is better.

So this is what I think.

The most powerful person in the AI era is not the one using the most tools.

Nor the one writing the most dazzling prompts.

It is the one who never loses what they have learned.

And that kind of person is, more often than not,

obsessed with keeping records.

https://youtube.com/@ai-gazua-lee