Generative AI
Generative AI denotes AI systems producing new content by themselves: text, images, audio, video or program code for instance. Unlike classic models, which merely classify or predict data, generative models learn the underlying patterns of large amounts of data and create new, original results from them. The best-known examples are large language models and image generators.
Also known as: generative AI, GenAI
How does generative AI work?
Generative AI rests on deep neural networks learning from enormous amounts of training data. The model internalises statistical patterns and relationships: which words typically follow one another or how image elements work together for instance. On that basis it can produce new content matching the patterns learned without simply copying them.
In text-based generative AI the model computes the most likely next element for a given input and assembles an answer word by word. Most modern systems use the Transformer architecture, which can capture connections across long passages of text. The quality of the results depends heavily on how the input is worded, which makes Prompt engineering into a discipline of its own.
What types of generative AI are there?
Generative AI comes in different forms depending on the content produced. Large language models generate text, answer questions, summarise documents or write program code. Image generators create photorealistic or artistic images from written descriptions.
Alongside these are models producing audio, speech, music or video. Multimodal systems combining several of these capabilities are spreading, able to understand images and write text at the same time for instance. This versatility makes generative AI one of the most consequential technological developments of recent years.
What opportunities and limits does generative AI have?
Generative AI can speed up work considerably by taking on routine tasks, producing drafts or supplying creative impulses. It helps with creating content, programming, research and communication and so makes knowledge and tools more widely accessible.
There are clear limits at the same time. Generative models can supply false or invented information, so-called hallucinations, and their results reflect the strengths and weaknesses of the training data. Careful review of the output and responsible handling of data protection and copyright therefore remain indispensable.
How do companies use generative AI?
In business, generative AI has many uses: creating marketing and web content, customer service through smart assistants, Software development as well as analysing and summarising large volumes of documents. Methods such as connecting to company-owned knowledge let the results be tailored further to the context at hand.
A well-considered strategy is decisive for successful use. Generative AI should be used deliberately where it improves concrete tasks, embedded in clear processes and with an eye on data protection and quality assurance. A fascinating technology thus becomes a dependable tool for everyday work.
Putting generative AI into practice with Elisabit
Elisabit helps companies use generative AI sensibly and safely, from the first idea through choosing suitable models to integration into websites, workflows and marketing processes. Concrete value is always to the fore, not technology for its own sake.
Whether automated content, intelligent assistants or connecting generative models to your own data: we build practical solutions fitting each company's goals and conditions. The start with generative AI thus succeeds in a structured, responsible way with measurable value.
Frequently asked questions
What is the difference between generative AI and classic AI?
Classic AI models classify data or make predictions, for instance whether an email is spam. Generative AI, by contrast, creates new content such as text or images. It does not only answer questions about existing data but produces new, original results itself.
Are the results of generative AI always correct?
No. Generative models can produce plausible-sounding but false information, so-called hallucinations. Their output should therefore always be checked critically, particularly on facts and sensitive topics. Generative AI is a tool for support, not a replacement for human oversight.
What role do prompts play in generative AI?
Prompts are the inputs with which you steer a generative model. Their wording affects the quality of the results considerably. Precise, well-considered input generally leads to better output, which is why prompt engineering has become a discipline of its own.
Is generative AI safe to use in a company?
With careful implementation, yes. Clear data protection rules, the choice of suitable models and providers and processes for quality assurance matter. Generative AI can thus be used responsibly and integrated into existing workflows without taking unnecessary risks.
Related terms
Machine learning enables systems to learn from data and make predictions without being explicitly programmed.
Deep learning uses deep neural networks to recognise complex patterns in large volumes of data automatically.
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
Prompt engineering is the craft of phrasing AI instructions so that language models return better results.
AI automation uses LLMs and agents to automate even unstructured business processes end to end.
Put AI to work for your business?
We help you integrate artificial intelligence into your processes, your marketing and your website — strategically and securely.
Your contact
Stefan
I look forward to hearing about your project and finding the best solution together.
