What is generative AI? How it works and what it means for you

Generative AI is a form of artificial intelligence that creates new content from an instruction: text, images, code or audio that did not exist before. So what is generative AI in practice? You describe what you need and the system composes the answer itself, instead of looking something up or selecting it. ChatGPT, Claude and Gemini are the best-known examples.

The word “generative” carries most of the meaning here. AI has been part of the software you use for years, from the spam filter to the text recognition that makes a scanned invoice readable, but those systems assess and sort. They do not create anything. That ability to produce is where the types of AI part ways.

The distinction has practical consequences. A system that classifies gives an outcome you can check against the source. A system that produces gives an outcome you have to judge on its content, and so the work shifts from doing to checking.

Below, you will read how generative AI arrives at its answer, what you can do with it in office work, why it is sometimes wrong, and what it means for confidentiality and liability once you use it with client information.

What is the difference between generative AI and the AI you already use?

Generative AI produces new content, whereas the AI already in your software mainly classifies and predicts. The difference is not how advanced something is, but whether the system retrieves something or composes something.

Three examples from your own workplace show the difference. The spam filter decides whether a message belongs in your inbox, so it chooses between two outcomes. The text recognition that turns a scanned invoice into searchable text recognises letters and numbers that are already there. The search suggestions in your case management system rank existing files by likely relevance.

None of these three makes anything up. They can be wrong, but the error is then a wrong choice among existing options, and you spot it as soon as you put the source next to it. With generative AI the output is new, so there is no source to compare it with unless you provide one yourself.

In practice, that means a different way of checking. You check classifying AI by sampling, because the system does the same thing every time. You check generative AI every time, because the same instruction can produce two different answers that both sound plausible.

Vendors do not always make that distinction visible. Almost every software package now puts AI on the label, while the feature behind it may just as well be a classifying model that has been there for years. The question that settles it is simple: does the system produce text or images that would not exist without that instruction? If not, little changes in how you need to check it.

How does generative AI arrive at its answer?

For text, generative AI predicts word by word what logically comes next, based on patterns in the data the model was trained on. At each step, the model calculates which continuation is most likely and writes it down, then repeats the same calculation with the new word added.

The model determines which words in your instruction matter by weighing each word against all the other words in the input. That mechanism is called self-attention, and it explains why a model can link “the deed I sent yesterday” to one specific document rather than to the word “deed” in general. The same weighting is why a long, messy instruction works worse than a short, well-organised one.

What matters about this design is what is missing. There is no checking step between the prediction and reality. The model does not assess whether something is true, only whether it fits the pattern, and those two things usually coincide, but not always. Put this next to the underlying mechanics of how AI works and you can see why fluent output says nothing about accuracy.

A second property is often misunderstood in practice. The model does not learn from your conversations. What you enter today does not change the model. A new conversation starts without knowledge of earlier conversations, unless you provide that context yourself or the tool stores it. ChatGPT and Claude store such context in projects and through a memory feature that carries information over from earlier conversations; as of October 2026, Claude’s memory feature is available on all plans, including the free plan. The model’s knowledge also runs up to a certain date, after which it has no information.

What can you do with generative AI in office work?

In office work, generative AI delivers the most on tasks that involve a lot of text and little judgement. Summarising documents, drafting a first version of a letter or report, restructuring a long text, having it argue against your reasoning, and writing or reviewing code are the uses where the time saved is visible.

Modern models handle more than text. OpenAI’s GPT models and Google’s Gemini models are multimodal, which means you can upload a photo and ask what is in it, supply a spreadsheet for a summary or have a recording transcribed. For images it also works the other way round: a description produces an image, which falls under AI art.

Just as important is what it does not do. The model has no substantive judgement about your field, has no information about your case unless you supply it, and gives a slightly different answer to the same instruction each time. For work where consistency is the requirement, such as a calculation that must come out identical every time, a fixed rule or a formula is more suitable than a model.

In my work as an SEO specialist producing AI content, I see that the quality of the input directly determines the quality of the output. A half-finished brief produces half an article that looks complete, and that is exactly the trap: the missing part does not show in the form.

One example shows both the gain and the limit. When you draft an advice letter, most of the time goes into wording and structure. With a model, less time goes into the first draft and more into refining it, and the thinking shifts to judging whether anything is missing. The letter does not get better by itself; the time moves to the part where your expertise counts.

Why is generative AI sometimes wrong?

Generative AI sometimes produces information that is factually wrong but convincingly worded. This is called hallucination. The model then makes up a source, a figure or a regulation, not because it is lying but because it picks the most plausible continuation rather than the demonstrably correct one.

The tricky part is that nothing in the wording gives it away. A made-up statutory provision arrives in exactly the same confident tone as a correct one, including a number that looks credible. If you only judge the output on readability, you will not see the difference, and the chance that an error slips through is highest on topics you know little about yourself.

A workable rule of thumb is to treat output as a first draft, not a finished product. Check facts, figures and references at the source before they end up in a report or in client communication, and plan that step as part of the task instead of leaving it to whoever happens to have time.

What does generative AI mean for confidentiality and liability?

Generative AI affects confidentiality the moment you enter something, because everything you put in a prompt is processed on the provider’s servers. For an office that works with client data, that is not a technical detail but the first question that needs an answer.

The compliance thinking from my years at major banks translates into three questions for every AI task: what data goes in, which claims do you base on the outcome, and who is responsible for the result. With generative AI, the first carries the most weight, because entering data is already processing, regardless of what you do with the answer.

The account type determines the terms under which that processing takes place. A free consumer account comes with different terms from a business version with a data processing agreement, and with some account types, the information you enter may be used to improve models. Check for each tool which account type your office actually uses, because in practice employees often still work on a personal account from when they first tried it out.

For regulated professions, professional secrecy comes on top of that, and it sets a limit that is separate from what the vendor technically allows. Many tasks remain possible once names and amounts are anonymised; where that is not possible, the answer should be an explicit no. Which information you do and do not enter is part of responsible AI use and should be set out for each type of task, not weighed up by each employee at a busy moment.

Since February 2025, this has also been directly covered by legislation. Under Article 4 of the EU AI Act, organisations must take measures to support the development of AI literacy among their staff, in the wording that has applied since 27 July 2026. Since 2 August 2026, transparency requirements under Article 50 have applied to content created with AI. Those obligations fall under AI Act compliance, together with the agreements on processing and record-keeping. In every case, liability for the outcome stays with the professional, not with the provider of the model. A course such as the AI Literacy Course, in which employees learn to check generative AI on their own work, is one of the measures Article 4 has in mind.

What limitations does generative AI have?

Generative AI has three limitations that do not go away with a better model. The first is the knowledge cut-off: the model is trained on data up to a certain date and has no information after that, unless it can search or you supply the information.

The second is the lack of your context. The model does not know your clients, your procedures or the agreements you made last year. Anything it says about your situation that you did not supply is an assumption, even if it happens to turn out right.

The third is bias in the training data. Whatever is under-represented or skewed in that data comes back in the output, without warning and in the same neutral tone as the rest. Not every task has the same risk profile, so this matters less for an internal memo than for a text that contains a judgement about people.

How do you get started with generative AI in your office?

With generative AI, start with one process, not with a tool. Choose a task that comes up often and takes a lot of time, decide which information it needs and which of it may be entered, and agree who checks the output before anything goes out.

That order prevents the two most common mistakes. The first is taking out a subscription and hoping employees will do something with it themselves, which leads to a handful of enthusiasts and a large group who leave it alone. The second is setting up an organisation-wide strategy before anyone has practical experience.

I translate the four-eyes principle from banking into AI work as a fixed step: no output that matters goes out without a second check. The approach I use deliberately starts small, with one to three processes, so that what you learn in the first process makes the second one faster.

Agree in advance how you will tell after a month whether it works. How much time did the task take before, how often is the output now changed before it goes out, and does that add up to a net gain? Without that point of comparison, the discussion comes down to gut feeling, and that is a poor basis for the next step.

Offices that want to set this up in a structured way, with agreements for each type of task and checkpoints that remain the office’s own, will recognise this as the basis of fixed team agreements on AI. The choice of tool does not come first; it follows from the processes you have picked.

Frequently asked questions about generative AI

Besides how it works and its risks, generative AI raises questions about where the technology comes from and how to recognise content it has created.

How long has generative AI been around?

Generative AI in its current form emerged in the mid-2010s, when researchers built models that create new examples themselves. In 2014, Ian Goodfellow and colleagues described generative adversarial networks, and in 2017 came the transformer, the architecture behind today’s language models. The general public got to know generative AI when OpenAI launched ChatGPT on 30 November 2022.

Is generative AI the same as an LLM?

No, generative AI is not the same as an LLM, because a large language model is one type of generative model, focused on language. Google describes an LLM as a language model with a very large number of parameters, in practice usually built on the transformer, such as Gemini or GPT. Generative AI also includes models that create images or sound without being a language model.

How can you tell whether an image was made with generative AI?

The most reliable way to recognise an image made with generative AI is a digital watermark, because the image itself gives little away. Google uses SynthID to place invisible watermarks in content from its AI models, and in the Gemini app you can upload an image or video and ask whether Google AI created or edited it. If that check finds no watermark, it does not prove a person made the image, because SynthID only looks for the watermark of Google and its partners.