AI stands for artificial intelligence. It is the technology by which computers perform tasks that require normal human thinking skills, such as understanding text, recognising patterns and answering questions. The term is widely used, from simple recommendation algorithms to sophisticated language models like ChatGPT.
What does the abbreviation AI mean?
AI is the abbreviation for “artificial intelligence”. The term was first officially used in 1956 by US computer scientist John McCarthy, who thus gave the field a name.
Artificial refers to the fact that intelligence is built by humans, as opposed to the biological intelligence of humans and animals. Intelligence refers to the ability to learn, reason and solve problems. Together, the term describes systems that simulate that ability through software and computing power.
In everyday parlance, AI is used as an umbrella term for everything from smart computing systems, from a spam filter to a self-driving car. Technically, these are all forms of narrow AI: systems that perform one specific task. You can read more about the different forms in our article on the types of AI.
What does generative AI mean?
Generative AI is the form of AI that produces new content based on an instruction. You describe what you want, and the system generates text, images, code or audio. Tools like ChatGPT, Claude and Gemini are examples of generative AI.
The word “generative” refers to the generation of new output, as opposed to AI systems that only classify or predict based on existing data. Generative AI has most directly changed the field of work for professionals, as it makes tasks like writing, summarising and analysing accessible without technical knowledge. You can find a full explanation in our article on what generative AI is.
What does LLM in AI mean?
LLM stands for Large Language Model. It is a specific type of AI system trained on huge amounts of text and therefore capable of understanding and generating human language.
ChatGPT, Claude and Gemini are all three LLMs. They are trained on hundreds of billions of words from books, websites and other text sources. Based on that training, they learn to recognise patterns in language and can answer questions, write texts and summarise documents.
The word “large” refers to the scale of the model: both the amount of training data and the number of parameters, the internal settings the model works with. Larger models tend to be more capable, but also require more computing power. How an LLM works technically explains our article on how AI works from.
What does prompt mean in AI?
A prompt is the instruction or question you give to an AI system. It is the input based on which the system generates its output. In text generation, a prompt is the text you type into tools like ChatGPT.
The quality of your prompt directly affects the quality of the output. A vague prompt produces a generic response. A specific prompt with context, purpose and desired formatting produces useful, focused output. Learning to formulate effective prompts is called prompt engineering and is a concrete skill that professionals can develop.
Good prompts contain at least three elements: context about your role or situation, a clear goal, and a desired form for the output. In the ChatGPT course from LearnLLM Learn step-by-step how to write prompts that produce consistent, actionable results for your field.
What does bias mean in AI?
Bias in AI refers to systematic errors or biases in the output of an AI system caused by skewness in the training data. If an AI is trained on data that underrepresents or misrepresents certain groups, the model produces output that reproduces that skewness.
A concrete example: a job application system trained on historical hiring data may systematically score women lower if that data was from a period when men were more often hired. The system has no intention but reproduces the pattern that was in the data.
For professionals, this is relevant because AI output is not neutral. It reflects the data on which the system has been trained, including the biases present in it. Critically assessing AI output is therefore a skill, not an option. You can read more about the risks associated with AI use in our article on the risks of AI.
What does agentic AI mean?
Agentic AI refers to AI systems that independently perform multiple steps in a task, without the user having to direct each step. An agentic AI system can be given a goal, create its own plan and then perform actions to achieve that goal.
The difference with an ordinary chatbot: a chatbot answers one question at a time and waits for the next instruction. An AI agent makes its own decisions about the order and content of the steps needed to complete a task. This can range from conducting online research independently to scheduling and sending e-mails.
Agentic AI is a relatively new development that is rapidly gaining momentum. For professionals, it is a term that is increasingly appearing in discussions about AI automation and the future of knowledge work.
What does AI mean for professionals in practice?
In practice, AI means that some of the work professionals do every day can be done faster and cheaper. Writing texts, summarising documents, structuring information and answering questions are tasks where AI adds immediate value.
At the same time, AI also means new responsibilities: assessing output for accuracy, being aware of privacy and data, and understanding what a system can and cannot do. Professionals who have that basic understanding work more effectively with AI than those who treat the tools as a black box.
Want to build a good understanding of what ChatGPT is and how to deploy it? Our comprehensive article lays the groundwork. For a broader overview of what AI means as a technology, read on in our article on what artificial intelligence is.
