# Speech-to-Text: The Basics of Using AI

*Formatted English version of the raw German transcript recorded 2026-08-11 — language smoothed, repetitions merged, deliberately kept rich. Nothing invented.*

## What this is about

With this transcript I want to show and make clear how easy this is — and that it is the module you should start with. My idea: I want to draw people's attention to something that, in my opinion — as long as no technology exists that is superior to it — is the fastest way to capture what is in your head and write it down: **speech**. With what you speak out loud, you are considerably faster than when you write it.

That doesn't mean speaking is the best tool of choice in every case and under all conditions. But it makes an essential difference for the question of how you will use AI and LLMs in the future — and what already sets you apart from other users today, and even more so tomorrow. Whoever doesn't clear this hurdle early, become aware of it and practice it, will be at a disadvantage later.

The real exercise behind it is this: getting better and better at describing things, formulating them, getting to the bottom of them — and gaining clarity about what you actually want right now. The more often you do it and the deeper you go, the more often you see which kind of information leads to which results, the more efficiency, speed and ease you develop in working this way — and a freedom you may not have been aware of before.

## Why speaking is faster than typing

You read figures like: people speak about five times faster than they can write. Even compared to the fastest typists — and the range there is huge, some write very slowly, some very fast — the following holds: even a very fast writer can *speak* far more content in the same time than they can type.

So it is a question of efficiency: getting what is in your head onto paper — or rather, into implementation. If you try to write down things that are more complex in content or topic, you simply won't get as far in the same time as if you recorded them, had them transcribed, and gave the LLM that wealth of information.

And I am very sure: someone at the keyboard or on the conventional path versus someone who gives input by voice, plans and orchestrates things and hands them to an LLM or an AI agent — the advantage simply sits clearly on one side. On that basis there is no need for lengthy comparisons, discussions or persuasion.

## The freedom in speech prompting

In my personal assessment: **there is no limitation** on the scope and length of what you record and speak. It is not a person who has to listen to you whenever you happen to feel like it — you can capture your thoughts at any time, have them written down and turned into something useful.

In the concept of speech-to-text — or speech prompting, if you like — there is also the freedom to sometimes just go in circles and repeat yourself, without it being a problem. You are allowed to ramble. Not that you have to — but the wealth of content you actually record, and the effort of explaining and describing something, make the difference in the quality of the result.

One honest caveat belongs here: if you repeat things in such a transcript that you don't want in the end, don't be surprised if they show up anyway. That costs some efficiency — you may have to touch things up, depending on how good the system is that you work with. But it remains a small price compared to the gain.

## When short and typed is still right

You have to distinguish between different projects and contexts. For smaller aspects, for corrections, or when you are being very specific, short inputs make sense — and there is efficiency in that too: if no long recording is required, don't make one. It is not the case that recording at length makes sense in every situation.

If you quickly want some page with certain content and you don't really care how it is implemented, you don't need to speak much into it either — then let yourself be helped, but don't be surprised about the result, and grant the LLM a certain freedom. It cannot know what you expect. Not yet: the better the models get and the more memory functions are added, the better they understand how you tick, what preferences and ideas you have — and can assist with less input. As long as that is not the case, my firm conviction stands: **with a bit more content you usually get better results.**

## The hurdle: spoken sounds different from written

I believe there is a certain set of hurdles that keep people from recording things. One of them: written texts are different from spoken ones. That is true — here I can relate. But I am of the opinion that it doesn't have to stay entirely that way.

There are good ways and means to stay true to yourself and still be more efficient than before, when you typed texts by hand and made slow progress — especially when a recording is supposed to become an email, a message or a presentation. Just this much as a spoiler: everyone will agree that it is harder to start with a blank page than when something is already there. That is why it is so helpful to speak thoughts out first, just as they come, have them structured, and then go over them yourself, changing and rephrasing. And there are ways to have the text phrased very close to your own writing style — but more on that in a tutorial of its own.

## More of your own input = a more individual result

It should be clear: **the more input and content you provide yourself, the more the result corresponds to you** — and to what you want to achieve and convey. The LLM can smooth content, polish the language, reduce repetitions and fill gaps.

But: if you let everything be filled only with what the LLM suggests, it is no longer anything special. It doesn't lead to the same good, individual result — just as too little of your own content doesn't either. Unless you are one of those who can convey concrete ideas with very little input; that is the next-level efficiency: describing complex topics quickly, precisely and to the point, so that the LLM builds exactly what you pictured in your head with little information. That is worth striving for and trainable — and it gets easier over time.

By the way, you don't always have to throw in the recorded content one-to-one: you can first have it formatted and structured and put it into a plan as an intermediate step — even though in most cases that isn't necessary. This transcript, for example, I pasted in directly.

## How accurate is transcription today?

Very accurate. Already today, transcripts in German or English work with well over 97 percent accuracy — even on local models. Most people without a particular impairment definitely have no problem getting a transcript that is accurate and well understandable.

And here, too, it helps to say a few more sentences: if you expressed something poorly or swallowed a word, the context corrects it or lets it be inferred. That minimizes errors. Afterwards, the content can be checked in various ways — for instance by asking for a summary and seeing whether the aspects that matter to you are included, instead of having to read or listen to the whole long recording again. Or you let the LLM implement the thing directly and see from the result how well it understood.

## Realistic expectations: one, maybe two iterations

I find it quite utopian to assume that something is perfectly finished on the very first attempt — even if you made an effort and explicitly named content, structures and tools.

But: if on the first attempt you reach a result that is largely sufficient — one you could simply use if you had no more time, especially since it is not something you earn money with or pitch, but something you give to acquaintances and friends — then it is usually already good enough, and better than self-typed texts with mistakes and hard-to-follow writing, where most people never quite get what the key point actually is. And all of that is already given on the first attempt.

Whoever has ambition gets the rest done with a second iteration: follow up once or twice, name what you notice or don't like — done. That is a pretty good result and pretty efficient. I dispute that, holding generally across many different people, it is possible better and faster.

Because people are different — in talent, vocabulary, expression, depth, the ability to get things to the point. The differences are huge. If you take all of that into account and want to choose a tool that helps get things finished, structured and implemented with an LLM — then in my view there is nothing comparable that is as sensible and as simple to name as: **transcription**.

## The basics — and why now

This topic is not new; it is widely promoted on the internet, and I believe many people are already aware of it. But I want to make sure it doesn't stay knowledge only, and actually gets applied. Because: whoever doesn't use this, or doesn't use it enough, is missing something — opportunities to realize things with the help of AI. And that would be a shame. I know of no reasons that speak against it.

I like to approach this mathematically: this is addition and subtraction, the little one-times-one on which I build. Before we dive into bigger projects and possibilities, I want to make it clear and unmistakable: you are not doing yourself a favor if you haven't started, or don't already use speech-to-text as a matter of course.

Whoever practices this overcomes mental barriers and skepticism through trying and applying — and over time is ready for whatever comes next, no matter what it is. Because you have to interact with the LLM and a device anyway, and it is obvious that speech is the most efficient way. The state of things is text-based LLMs: speech becomes text, and the LLM is excellent at turning that text into structured, formatted, summarized content — readable, checkable, usable.

It helps you achieve and implement more, faster — already today, and even more in the future given the foreseeable development of the technology. More modules will follow: the most important tools and most sensible approaches for using AI efficiently. This here is the foundation.

---

## A personal word

I carry a great deal in my head on any topic — and because of the sheer volume and the speed at which I see connections, it is incredibly hard for me to always set the right priorities. For me, AI is a great gift: I use it to prepare my content for others — so that time, effort and nerves are spared. Theirs, and mine too.

I see nothing wrong in that — as long as you read the content yourself afterwards, know what you are sending, and stand behind it yourself. It would be something else to send messages you have never read, trusting that the AI will achieve something. That would no longer be personal interaction.

On the often-voiced criticism that LLMs only produce an average: that is true — they are based on the content of the internet and align with it. That is exactly why using them is logical for me: I get to name my content at the depth I need — and it is prepared in a structured way, understandable for the majority, without overwhelming anyone. Placing all the many small aspects that matter to me — and that co-determine why one makes one decision or another — fluently in a few sentences is an art I do not master. But I have the LLM to help.

I have been told it is "impersonal" when I do it this way. The truth is: I invest *more* time in the person and the content, not less. In the recording I have the chance to name things in depth, to say how they are meant — and then to get help being understood, without overwhelming others with the sheer volume that costs them time, nerves and patience. Without this help I was often still not fully understood; there were follow-up questions that could have been avoided, long processes, frustration towards me — up to people avoiding starting topics with me at all, because a short answer is rarely possible from my side. I don't find that fair: when I recognize it, work on it, give my best — and my best is not enough. So I use today's technologies to turn this weakness into, perhaps, even a strength.

I am aware that texts can also be edited in such a way that it is barely recognizable that AI helped. But I want to be honest and open about it — I have no problem saying it. I see in it a blessing and a freedom that everyone can use: communicating complex, difficult topics, where you sometimes struggle for words, to another person in a structured, well-readable way. That is a sign of respect for the other person's time — and should suggest importance, not impersonality. Not that everyone has to do it; everyone is free. But I find it difficult to be judged for it, or to be given the feeling that it is not okay. I believe that rests on people not being aware of how challenging it fundamentally is for me.

I hope this is a first step towards more understanding — and at the same time a testimonial to what is great about the whole AI topic, in this "future is now" moment we are living in.

---

*More modules and recommendations will follow — this is number one.*