Generative AI

Roles that need it

• AI Business Analyst
• AI Transformation Analyst (Banking)
• Data Scientist
• Management Consultant
• Business Analyst

Skills it belongs to

• Applied generative AI in office work (prompting, evaluation, governance)
• Large language model applications

Related concepts

• Large language model (LLM)A large language model is a neural network trained on very large amounts of text to predict the next word, which lets it answer questions, summarise, translate and write code. LLMs are the most common type of generative AI and of general-purpose AI model under the AI Act.
• Retrieval-augmented generation (RAG)Retrieval-augmented generation is a technique in which a large language model first searches a trusted document set, such as internal policies, and then answers using the passages it found, ideally citing them. It reduces made-up answers and keeps content current without retraining.
• AI hallucinationAn AI hallucination is output from a generative model that sounds plausible but is false or unsupported, such as an invented regulation article or a wrong figure. Controls include grounding answers in sources, human review and testing against known answers.
• General-purpose AI modelUnder the AI Act, a general-purpose AI model is one trained on large data that can perform a wide range of distinct tasks and be built into many systems, as large language models are. Providers must supply technical documentation and a training data summary; models with systemic risk face extra duties.

Sources

• The Financial Stability Implications of Artificial Intelligence, FSB (external site) accessed 31 Oct 2026
• AI Act, regulatory framework for AI, European Commission (external site) accessed 31 Oct 2026

Updated 31 Oct 2026.