AI jargon explained: a plain guide for business owners
By Keith Hamilton · · 4 min read
This is AI jargon explained in plain English for business owners: what the common terms mean in practice, and the question to ask when a supplier uses each one. Conversations about AI arrive wrapped in talk of models, agents and tokens, and it is easy to nod along without being sure what is being offered. You do not need a technical background to make good decisions about AI, but you do need to know what these AI terms mean. Treat this as a short AI glossary for small business.
AI jargon explained: what is the difference between automation, AI and machine learning?
Automation follows fixed rules, AI handles work that is hard to write rules for, and machine learning is how most AI is built.
- Automation is software following rules that someone has set. If an email arrives from a particular supplier, save the attachment to a particular folder. It is predictable.
- AI is software that handles work such as reading an email written in a customer's own words, understanding speech or summarising a long document.
- Machine learning means the system learns patterns from large numbers of examples instead of being given rules.
The most useful business solutions combine the two: AI to read and understand, and ordinary automation to carry out the steps that follow.
The question to ask: which part of this is AI, and which part is ordinary automation? The automated parts are cheaper and more predictable, so you need to know where the AI is actually doing the work.
What is generative AI, and what is a large language model?
Generative AI is AI that creates new content: text, images, audio or speech. A large language model, shortened to LLM, is a type of generative AI trained on very large amounts of text. LLMs sit behind the chat assistants that most people have now tried.
An LLM works by predicting what text comes next. That makes it very good at writing, summarising and answering questions in natural language. It does not, on its own, look facts up or check them.
The question to ask: where does it get its information from when it answers?
What is a prompt?
A prompt is the instruction you give an AI tool. Clear prompts get better results. A good prompt says:
- who the answer is for
- what you want done
- what format you want it in
- what good looks like, with an example if you have one
What is AI hallucination?
An AI hallucination is when an AI tool states something that is wrong, usually in a confident and convincing way. It happens because the model produces plausible text rather than checking facts. It cannot be ruled out entirely, so outputs need checking, especially anything sent to customers.
The question to ask: what happens when it does not know the answer? A well-built system says so, or passes the question to a person, rather than guessing.
What are grounding and retrieval?
Grounding means giving the AI specific, trusted material to answer from, such as your own policies, price lists or website, rather than relying on its general knowledge. You will also hear this called retrieval or RAG (retrieval-augmented generation). The system finds the relevant passages in your documents and uses them to write its answer, with a link to the source.
This is how an internal knowledge assistant answers staff questions from your own documents.
What is the difference between training and fine-tuning?
Training is the process of building a model from data, and fine-tuning is further training on a smaller set of specific examples to adjust how the model behaves.
Both are regularly confused with grounding. Giving an AI tool your documents to answer from is not the same as training a model on them, and most small business uses need no training at all.
The question to ask: will anything we put into this tool be used to train your models or anyone else's? The answer matters for confidentiality and for data protection. Our UK GDPR and AI checklist covers what else to check.
What is an AI agent, and what are integrations?
An AI agent is AI that takes actions, not just replies. It looks up an order, books an appointment, updates a customer record or sends a text message.
To do that it needs integrations: connections to your other systems, usually through an API (application programming interface). An API is simply a standard way for one piece of software to talk to another.
The question to ask: which of our systems will it connect to, and what will it be allowed to change? Reading a record is very different from editing or deleting one.
What does human in the loop mean?
Human in the loop means a person checks, approves or steps in before something important happens. A tool drafts a reply that a member of staff approves before it is sent, or an AI voice agent passes a caller to a person when the call needs one.
The question to ask: at which points does a person stay in control, and how does the hand-over work?
What are tokens, and how does usage-based AI pricing work?
Tokens are the small chunks of text that LLMs process, roughly a word or part of a word. Many AI services charge by usage, whether that is tokens, minutes of calls or number of conversations, rather than a flat fee.
That means costs grow with volume. A service that charges 10p per conversation costs £100 at 1,000 conversations a month, and £300 at 3,000 in a busy season. The rate per unit stays the same; the bill triples.
The question to ask: what will this cost at our normal volumes, and at our busiest?
Where Forwardcycle fits
If you want your team to be confident with these terms and with the tools themselves, our Team Enablement workshops work on your own tasks. If you are weighing up what AI can do for your business, the free AI Readiness Assessment is a quick place to start, or you can arrange a free call and ask us anything, jargon included.