AI terms explained clearly

Fundamentals

These five terms come up the moment someone talks about AI:

• Artificial intelligence (AI): the umbrella term for software that solves tasks a person once had to think through, such as understanding text or spotting a pattern in numbers. When someone tries to sell you an “AI solution,” ask first which of these tasks they actually mean.
• Machine learning: the usual way an AI system gains its abilities, by reading many examples and deriving its own rules instead of following rules a person wrote by hand. For your business that means a system is only as good as the examples it learned from.
• Generative AI: a type of AI that creates new content itself, such as a piece of text, an image, or a summary. These are the systems behind tools like ChatGPT, which is where most SMEs meet AI first.
• Language model (LLM): the system behind text tools like ChatGPT, trained to predict the next likely word from a huge amount of text. It can feel like it understands you, but it is strong at wording and not automatically strong at checking facts.
• Training data: the examples a model learned from, such as text, images, or past customer conversations. Their source and quality decide what a system does well, so it is worth asking a provider where their training data comes from.

Terms from daily use

You hit these words the moment you use an AI tool yourself:

• Prompt: the brief you type into an AI tool, in your own words or from a saved template. The clearer the prompt, for example with an audience, a length, and an example, the more usable the result.
• Token: the small chunks a model splits language into, often word parts rather than whole words. The number of tokens usually sets how long a request can be and what it costs.
• Context window: how much text a model can hold in mind at once during a conversation. Once it fills up, the system loses earlier details, for example the figures from a long spreadsheet you pasted in at the start.
• Hallucination: a confident-sounding statement that is false or invented, such as a source that does not exist or a figure nobody checked. Every AI output needs a check before it goes into a quote, an email, or a decision.
• Multimodal: a system that understands several kinds of content at once, such as a photo, spoken language, and text in one request. That lets you, for example, upload a photo of a delivery note and have the details read out directly.

Tools and patterns that get mixed up

These terms describe how AI gets used inside a business, and they get confused often:

• Chatbot: answers one input, then waits for the next. A person still takes the following step. A chatbot makes no decision of its own.
• Copilot: supports a person directly inside their own work, suggesting wording, summarising, or correcting. The person stays in charge and accepts or rejects the suggestion.
• AI agent: chooses and carries out several steps on its own within a set frame, for example checking a request, drafting a reply, and creating a task. Because it decides more on its own, it also needs more control and clear limits.
• Automation: follows fixed rules set in advance, with no AI involved. For a repeating, clearly defined process it is usually more precise and cheaper than an AI solution.
• RAG (Retrieval-Augmented Generation): a method where a model first looks things up in your approved documents, such as product sheets or internal policies, before it answers. This cuts down invented statements because the answer stays tied to your real material.
• Fine-tuning: an already trained model gets adjusted with extra, often internal examples to fit a specific task or tone. It is usually worth doing once a plain prompt or RAG is no longer enough.

Why the exact terms help

Sources and further reading

• European Commission: AI literacy questions and answers
• NIST: AI Risk Management Framework