Large language models, or LLMs, are AI systems trained on huge amounts of text, so they can read a request in plain words and write a useful reply.
A large language model, or LLM, is an AI system trained on a huge amount of text, so it can understand a request in plain words and write a reply. LLMs sit behind tools like Claude, ChatGPT, and Gemini, and behind the AI helpers (the industry calls them agents) we set up for small teams. They’re good at drafting, summarising, sorting, and answering questions from information you give them. They’re not a database of facts, and they can sound sure when they’re wrong. That’s why we give them your own information to work from and keep a person checking anything that matters.
How large language models work, in plain terms
An LLM learns patterns in language from the text it was trained on. When you give it some text, it predicts what should come next, one small piece at a time. That plain mechanism, at a very large scale, is enough to draft an email, answer a question about a document, sort an enquiry, or pull the totals out of an invoice.
It also explains the limits. An LLM doesn’t look facts up the way a spreadsheet does. It writes the most likely reply to the text in front of it, so it can be fluent and wrong at the same time. The fix is to give it your own information to work from, like your prices, policies, and past orders, and to keep a person checking anything that goes to a customer. You can read more about the wider family of tools in our entries on generative AI and conversational AI.
How we use them
We use LLMs as one part of a bigger setup, not as the whole answer. For a small team, that usually means an AI helper connected to your inbox, store, or booking system, with better instructions (the industry calls it prompt optimisation) so it gets the answer right more often and costs less to run.
We work with a three-person online skincare store where the founder spent most evenings on order emails. An AI helper now reads each email, looks up the order in Shopify, and drafts a reply with the tracking link for the founder to check and send. Our first version sent simple replies on its own, until it answered a customer whose jar had arrived broken with a cheerful tracking link. Now every reply waits for a person, and returns still go through by hand.
Your data and instructions sit apart from any one model, so we can move them to a better one as things change. We also set them up so your data stays private, which our using AI safely service covers.