A large language model (LLM) is a type of AI trained on vast amounts of text to understand and generate human language. LLMs are the underlying technology behind tools such as Microsoft Copilot and ChatGPT, enabling them to answer questions, summarise documents, draft emails and hold natural-sounding conversations.
How large language models work
LLMs are trained on enormous datasets of text, learning statistical patterns about how words and ideas relate to each other. When given a prompt, the model predicts the most likely sequence of words to follow, generating fluent text in response. This process explains both the strengths of LLMs, fluent and often genuinely useful output, and their key limitation: a tendency to occasionally produce confident but incorrect statements, often called AI hallucination, because the model is predicting plausible text rather than retrieving verified facts.
LLMs in practice for UK businesses
- A business uses an LLM-powered Microsoft Copilot to draft first versions of proposals and reports, then reviews and edits the output before sending.
- A customer service team uses an LLM to summarise long email threads into a short briefing before a call, saving time without needing to read every message.
- A finance team treats LLM-generated cash flow commentary as a useful starting point that is reviewed against the actual figures before being included in a board report.
- A technology team builds a retrieval-augmented generation solution that combines an LLM with verified internal documents, reducing the risk of incorrect answers compared to using the LLM alone.
How Advantage helps businesses use LLM-powered tools responsibly
Advantage helps businesses understand where LLM-powered tools like Microsoft Copilot genuinely add value, and where human review remains essential, particularly for financial, legal or compliance-sensitive content. We build this guidance into our AI readiness and adoption work.
Frequently asked questions
Are large language models the same as search engines?
No. Search engines retrieve and rank existing web pages. Large language models generate new text based on patterns learned during training, which means they can produce fluent, plausible-sounding answers that are sometimes factually incorrect, a behaviour known as hallucination. This is an important distinction when relying on LLM output for business decisions.
Why do large language models sometimes give incorrect answers?
LLMs generate text based on statistical patterns learned during training rather than looking up verified facts in real time. This means they can produce confident-sounding but incorrect information, particularly for very recent events or niche topics outside their training data. Techniques such as retrieval-augmented generation help ground responses in verified source data.
Do large language models understand language the way humans do?
Not in the way humans understand language. LLMs identify statistical patterns in vast amounts of text to predict likely next words, which produces remarkably fluent and often useful output, but this is a fundamentally different process from human comprehension and reasoning.
