Have you ever used ChatGPT, Microsoft Copilot or any AI-powered search engine? Then you've already met an LLM, which stands for Large Language Model.
In simple terms, an LLM is a type of artificial intelligence that’s been trained on a huge amount of text. By learning patterns in language, it can better understand what you say to it and respond with more human-like answers.
These systems help power chatbots, virtual assistants, AI-powered search tools and productivity software. Here’s a closer look at LLMs and how they help make tech easier to use.
How did LLMs become so popular?
LLMs have taken off because they make AI genuinely useful. Ofcom reported that ChatGPT, one of the biggest LLMs, had 1.8 billion UK visits in the first eight months of 2025, up from 368 million during 2024. Instead of learning complicated software, you can ask questions as you would to a person and get quick, detailed answers.
Today, people use LLMs to do everything from writing emails to planning trips. Businesses use them to automate routine tasks and improve productivity. Advances in computing power and access to more data has helped make these tools faster, more capable and more widely available.
Lots of us use LLMs daily without maybe realising it. Whether that's through voice assistants, AI-enhanced search engines or productivity software integrated into our devices. To learn more about how AI works in modern computers, check out what is an AI PC.
How do LLMs work?
Think of an LLM as a supercharged version of your phone's predictive text. It's trained on massive amounts of text from books, articles, websites and other sources to learn how language works and how words, phrases and ideas connect. LLMs combine machine learning and natural language processing (NLP) to understand and generate human language.
When you enter a prompt, the model breaks your text into smaller pieces called tokens. These can be whole words, parts of words or even punctuation. It then looks at the context and predicts the words most likely to come next, generating a response that feels natural and relevant.
After its initial training, an LLM can be fine-tuned for specific tasks. This could include customer support, coding assistance or workplace assistants like Microsoft Copilot. While these models can follow conversations and understand context, they don't think or reason like humans. Instead, they generate responses based on patterns they've learned during training.
What are LLMs used for?
LLMs make it easier to get things done, such as:
Content creation – drafting articles, posts and captions
Summarising long documents and reports
Translating text between languages
Coding and programming help – from debugging to explaining concepts
Customer support and virtual assistants that answer in real time
Search and information discovery with conversational follow-ups
Education and learning, helping explain concepts or answer follow-up questions
You don't necessarily need a high-spec laptop to start using AI tools either. The Lenovo IdeaPad Slim 3i 14" Chromebook Plus comes with Google Gemini features built directly into ChromeOS, so it’s easy to get AI assistance. Meanwhile the Acer 315 15.6" Chromebook lets you use web-based AI tools such as Gemini and ChatGPT straight from the Chrome browser, with no extra software needed.
What is the difference between an LLM and generative AI?
The terms are often used interchangeably, but they're not exactly the same thing.
Generative AI is the broader category. It refers to AI systems that can create new content, including text, images, audio, video and code.
Large language models are a specific type of AI model designed to understand and generate text.
So an LLM is just one of the technologies that powers many generative AI tools.
LLM vs traditional search engines: what's the difference?
While both help users find information, they work very differently.
| Function | LLM | Traditional search engine |
|---|---|---|
| How it works | Generates responses using learned language patterns | Finds and ranks webpages by keywords |
| User interaction | Natural, conversational prompts | Keyword-based searches |
| Response format | Direct answers and explanations | Links to sources |
| Context awareness | Remembers context in a conversation | Limited contextual understanding |
| Complex questions | Handles multi-part follow-up questions | Often needs multiple searches |
| Content creation | Can draft text, summaries and ideas | Doesn't create content |
| Productivity support | Assists with planning and writing | Primarily helps find information |
| Source access | Uses learned patterns and connected sources | Directly links to websites |
In reality, most people use a mix of both. Search engines are still the best way to find original sources and websites, while LLMs are useful for summarising and answering more complex questions. LLMs can be great for quick responses, but search engines are the best way to verify information.
What are some advantages and limitations of LLMs?
Like any technology, LLMs have strengths and weaknesses.
Benefits of LLMs
The biggest benefit of LLMs is that they can help save time. This includes:
Improved productivity – get writing, research and admin tasks done faster
Natural interactions – ask questions the way you actually speak
Less repetitive work – speed up emails, notes and document creation
Creative support – generate ideas, outlines and first drafts
Tailored assistance – get responses based on your specific needs
Many of these benefits can be seen in AI assistants such as Microsoft Copilot, which can help with writing, research and everyday tasks. Find out more in our guide to what is Microsoft Copilot.
Limitations of LLMs
While LLMs can be really useful, they do have their drawbacks. It's important to understand where they can fall short, such as:
They can generate inaccurate or misleading information
Knowledge may be outdated, depending on when the model was trained
Potential bias in responses based on training data
Limited real-world understanding and common-sense reasoning
May struggle with highly specialised or niche subjects
Requires human verification for important decisions
Privacy risks if sensitive information is shared
Shouldn’t be relied upon as the sole source of information
Can occasionally misunderstand prompts or context
Always treat AI output as a first draft, not a final answer. AI is best used as a powerful assistant rather than a replacement for human expertise.
Beyond accuracy and privacy concerns, it's worth knowing the environmental impact of AI too. Find out more in our guide to does AI use a lot of energy.
Are LLMs safe to use?
In most cases, yes. But like any online tool, it's important to use them responsibly. Avoid sharing sensitive personal information, double-check important facts and remember that AI can sometimes make mistakes. For important decisions, always verify information using trusted sources. If you want to know more, find out what happens to your data when you use AI.
Looking for an AI laptop?
If you're thinking about upgrading, our guide to the best laptops for AI explains what to look for and some of the best options out there. Such as premium laptops like the Apple MacBook Pro 14" and Samsung Galaxy Book6 14" Copilot+ PC, which are both designed to handle more demanding AI-powered tasks.
Need more help?
Check out our range of AI laptops or our wider guide to AI for more info.
FAQs
What's the difference between LLM and AI?
AI is the broad field of technology that allows machines to perform intelligent tasks. An LLM is a specific type of AI designed to understand and generate human language.
What is the difference between LLM and GPT?
GPT is a specific family of large language models. While all GPT models are LLMs, not all LLMs are GPT models. Put simply, ‘LLM’ is the broad category, while ‘GPT’ is one particular type within it.
Are LLMs really useful?
Yes. LLMs can help with writing, research, summarising, translating, coding, planning and many other everyday tasks.
What are some examples of real-world LLMs?
Examples of real-world LLMs include GPT models, Google Gemini, Anthropic Claude, Meta Llama and the language models that power tools such as Microsoft Copilot.
Can LLMs actually learn?
LLMs learn during training and fine-tuning. However, they don't continuously learn from each individual conversation in the same way humans learn from experience.
