In AI-assisted coding, prompts are becoming very important. A prompt is what you give to the AI so it knows what to do. Like giving instructions to a new worker. If the instruction is good, the result is good. If the instruction is bad, the result is bad.

Today, many developers are already using AI tools to create code. But there is one problem: very often the Git repositories are a mess. You find the code but no real documentation. You don’t know who wrote what, or why. Sometimes code is broken. Sometimes it is not even clear what the app is doing.

This happens because the code is not really written like before. It is generated by AI, and often in pieces. People try things, delete things, test again. They copy-paste. They do not document. And most of all, they do not save the prompts.

Without prompts, it is hard to understand what the AI was told to do. Code might look clean, but it lacks the background. Why was this function added? What was the use case? What did the developer ask the AI? We are missing that story.

Some might ask, why not just use “undo” or check Git history? But that doesn’t solve the real issue. Undo shows the change in code, not the change in thinking. Git history shows code diffs, but it doesn’t show the context. The prompt is the context.

AI coding is not like human coding. The developer is not writing every line or no line at all. They are guiding the AI, testing, and iterating. That guidance happens through prompts. If we only keep the final code, we lose the whole journey. We lost the way we got there.

There are also practical problems. Let’s say you generate a component with AI. Later, you want to generate a similar one. But you forgot what you asked the AI. You try to remember, but the result is different. Or worse, you can’t recreate the behavior. It’s frustrating. It slows you down. You lose trust in the process.

And even if you do remember the prompt exactly, AI models often produce different results from the same prompt. Even with the same large language model, the outputs can vary. AI has a tendency to be creative. That is part of its power, but also a risk. This means that prompt history is not just useful—it might be essential. It gives you a baseline. A way to understand and compare. A way to see how things change, even when the input stays the same. You may ask, what is the point of having the same prompt then? What I mean is, if the prompt is versioned and quality is good, probability to repeat the same result will be higher.

If you had the prompt saved, you could just reuse it. Maybe tweak a little. Or improve it. Maybe you could look at how the prompt evolved over time. What worked, what didn’t. This is how learning happens. This is how quality improves. So this leads to a question: should we version control the prompts?

I say yes!

Prompts should be saved. Prompts should be tracked like code. Like Git tracks code versions, we should track prompt versions. Then we can see the changes. We can understand what was tried. We can go back. We can improve.

This does not have to be complicated. Start simple. Create a “prompts” folder in your project. Save your prompts there as text files. Add date and short description. Or use tools like GitHub Copilot Chat, which sometimes show history, but that is not yet reliable. We need better tools.

Maybe in the near future, prompt history is more important than code diffs. Because code can be generated again, but the thinking behind the prompt is harder to guess. Prompts are not just throwaway commands. They are design, they are architecture, they are process.

Imagine a world where every feature, every component, has its prompt history. You can trace back the evolution. You can analyze what kind of prompting works best. You can even train your own models better.

Simple tools can be made. Like prompt log inside project folder. Even better, prompt versioning tools that work with Git. Like a commit message, but for prompts. Automatic saving of prompts used. Structured format. Maybe even visual history. This will help teams and solo developers. Teams can understand each other’s thinking. New team members can onboard faster. Audits become easier. Debugging becomes more clear. And yes, we can recreate code faster and better.

AI coding is great. But without structure, we just create a fast mess. Prompts are part of that structure. They are the new source code in many ways.

When we try to generate code with AI, we must learn and understand the right methods for controlling workflows. It’s not just about giving a prompt and seeing what happens. It’s about knowing how to guide, adjust, repeat, and evaluate. Creativity is mostly a human strength, but AI does perform surprisingly well in creative areas. It uses our data, our ideas, our language. But it does so relentlessly. It can try many concepts fast. Then it selects what fits the situation.

Here, we need control. Not control to limit AI, but control to guide it. To make sure its creativity stays productive. To make sure the outcomes are reliable. This is why structure is needed. This is why version control for prompts is not just nice, it is necessary.

We must start building the methods and habits to control this process. Think about it like managing a team of junior developers. You don’t just give them a task and disappear. You check in, you set expectations, you document decisions. Same goes with AI. The clearer and more repeatable our instructions and processes are, the better the results. This means prompt discipline. Logging, tracking, comparing. Not just generating code, but learning how we generate. That’s the way to build trust into the process and make AI a reliable tool for everyday development.

One more thing before I quit. I think we need to clear something up. There is a bit of misunderstanding—or maybe just a different point of view between AI-prompting coders and traditional developers and this will often lead to pointless fights. When we talk about AI-assisted coding, people see things in very different ways.

On one side, we have the prompt-based creators. They are excited by the speed, the creativity, and the flexibility of AI. They don’t worry too much about structure. They are focused on building fast, testing fast, and moving fast. They see the AI as a partner that helps them experiment.

On the other side, we have traditional developers. They look at the technical side. They worry about broken builds, unclear logic, untestable code. This is the legacy, this is the real deal coming from bad designs or programming. For them, AI is unreliable. It creates code that is hard to maintain and may contain strange solutions or unexpected bugs. They don’t see the fun, they see the risks. For a reason.

And both sides are right. But we should not argue about “Yes, it can” and “No, it cannot.” Because one thing is for sure: AI will get better. Fast. It will become more reliable. It will succeed where it now stumbles. The real question is not about AI’s current limits. It is about how roles and processes are changing.

This is the moment to talk about that change. How we structure our work. How we define responsibilities. How we guide AI. How we combine creativity with discipline. The future of development is not about replacing anyone. It is about new ways to work together — human and machine.

But I must say, this makes me feel a bit dizzy. Thinking all of this, it can sound like AI is some independent, living thing. Like it’s doing things on its own. That’s not true, of course. AI is just a tool. A powerful one, yes, but still a tool. We humans are in control. Or at least, we should be.

But yes, version control your prompts. It is a small effort. But big help. In time, it will feel just as natural as committing code to Git.

Mikko