What is generative AI?

Generative AI: definition

While classic AI mainly recognizes or classifies, for example “spam or not spam,” generative AI creates something new: an answer, a product image, a summary, a voice output or a program function. It became well known through chat assistants such as ChatGPT, Claude or Gemini, which are based on large language models. For companies this means the same technology can draft texts, summarize documents or suggest program code.

Generative AI exists for many media. Text models write and translate, image models create graphics, other models produce speech, music or video. Increasingly, single models process several formats at once; these are called multimodal models. What matters is not the medium but the question of which task in your business can be done faster or better with it.

How generative AI works

A language model is trained on very large amounts of text and learns which words are likely to follow in which context. When answering, it continues word by word with what fits best. Image models usually work with diffusion methods: they gradually shape an image from random noise that matches the description. The results are calculated, not looked up—one reason they must be checked.

Quality depends heavily on the instruction, known as the prompt. In addition, a model can be given your own knowledge, such as product catalogs or manuals, so that answers fit the company and are not based only on general training knowledge. The more precisely task, audience and format are described, the more useful the result.

Real-world example: texts and quotes at a trades business

A plumbing company uses generative AI for drafts: a cover letter for a quote is created from keywords about the job site, a reply template from customer emails, a short summary from long manufacturer documents. The owner reads every draft, adds prices from the costing system and sends it only after approval.

This saves employees time on wording while they keep responsibility and tone. Prices and commitments deliberately do not come from the AI but from the costing system. This clear division of tasks prevents the most common mistakes. The time savings show up mainly in recurring writing tasks such as quote texts, service descriptions or appointment confirmations.

Opportuni­ties, risks and imple­menta­tion

Generative AI speeds up drafts, research and routine replies. However, it can formulate falsehoods convincingly and can pose a data protection risk with confidential data. Copyright questions for generated images and texts are also not conclusively settled. Under the EU AI Act, AI-generated content must be labeled under certain conditions.

Nano Concepts integrates generative AI into your applications, with clear rules for data flow, approval and logging. On request we start with a small pilot for one use case, such as text drafts or summaries from your documents, and expand only after an evaluation.

• Do not enter confidential or personal data into public tools
• Check results for accuracy before passing them on
• Set internal usage rules and training for employees
• Document use and check labeling obligations
• Choose providers by processing location and a data processing agreement