What Is Generative AI? Definition, Uses and Limits

Generative AI in Brief: Definition and How It Works

Generative AI refers to models that produce new, plausible content rather than only analysing existing data. They are trained on very large datasets and learn the statistical structure of language, images or sound. From an input, called a prompt, the model computes the most likely continuation step by step and thereby assembles a new result.

Most of today's systems are based on the transformer architecture. For text, the model splits input into tokens and predicts the next token each time; for images, diffusion models are common, gradually shaping an image from random noise. Such broadly trained base models are called foundation models and can be reused for many tasks.

Generative AI vs. Predictive AI: The Core Difference

Both belong to machine learning but pursue different goals. Predictive AI answers questions like -What will happen?- or -Which category does this record belong to?-. Generative AI answers -Create something new-. In practice, the two are often combined.

• Goal: predictive AI forecasts or classifies; generative AI creates new content.
• Output: predictive returns numbers, probabilities or labels; generative returns text, image, audio or code.
• Typical cases: predictive for fraud detection, demand forecasting, scoring; generative for drafts, summaries, assistance.
• Evaluation: predictive models are measured by accuracy against ground truth; generative outputs additionally need human quality and fact checks.

What Generative AI Creates: Text, Image, Audio, Video, Code

• Text: drafts, summaries, translations, Q&A and structured extraction from documents.
• Images: illustrations, product visuals, variations and text-driven image editing.
• Audio: speech synthesis, voice-overs, transcription and music or sound-design sketches.
• Video: short clips, animations and storyboards from text or image prompts.
• Code: code generation, explanation, refactoring and test drafts as assistance for developers.
• Multimodal: modern models process and combine several formats, e.g. image plus question into a text answer.

Business Use Cases with Clear Value

Generative AI delivers the most value where a lot of text and knowledge is handled and a human reviews the result. It works best as an assistant that produces drafts and speeds up routine work, not as unchecked automation.

• Marketing and content: first drafts, variations and multilingual localisation.
• Customer service: reply suggestions, knowledge-base search and conversation summaries.
• Knowledge work: research, summarising long documents and question-answering over your own data via retrieval-augmented generation.
• Software development: coding assistance, documentation and test creation.
• Internal processes: structuring and preparing forms, reports and minutes.

Limits, Risks and Common Pitfalls

• Hallucinations: models can state false things convincingly. Always verify facts against reliable sources.
• Bias: training data carries biases that can propagate into outputs.
• Recency: models only know events after their training cut-off if current data is connected.
• Copyright and confidentiality: clarify the provenance of training and input data and the rights to outputs.
• Reproducibility: the same input can yield different outputs, which complicates audits without fixed settings.

Responsible Use from a Swiss Perspective

In Switzerland, personal data is governed by the revised Data Protection Act (revDSG, also nFADP), supervised by the FDPIC (EDOEB). Anyone using generative AI with personal data should clarify purpose, legal basis, data minimisation and transparency, and check where processing takes place. Organisations with EU exposure are additionally affected by the EU AI Act.

In practice, a human-in-the-loop works well: people review outputs before publication or decisions. Clear policies, suitable vendor contracts and staff training reduce risk without giving up the value.