Large language models (LLMs) predict the next word in a sequence, trained on billions of words from across the internet
They power ChatGPT, Claude, Perplexity, and Google’s AI Overviews
Text becomes numbers (tokens), then mathematical patterns generate human-like responses
LLMs don’t understand or think; they recognise and reproduce patterns
Understanding LLMs helps you create content that AI search tools can find and cite
Large language models work by predicting the next word in a sequence, trained on billions of words to recognise patterns in human language.
Every time you ask ChatGPT a question or see an AI-generated summary at the top of Google, you’re interacting with one of these systems. They’ve become part of how millions of people find information, yet most business owners have no idea what’s happening behind the scenes.
The good news? The core concepts are far simpler than the jargon suggests.
This guide explains how large language models work using everyday examples, giving you the foundation to make smarter decisions about AI and your online presence.
Table of Contents
What Is a Large Language Model?
A large language model, or LLM, is a type of artificial intelligence designed to understand and generate human language.
Think of it as an extraordinarily sophisticated autocomplete system. When you type a message on your phone and it suggests the next word, you’re seeing a basic version of what LLMs do.
The “large” in the name refers to scale. Modern LLMs contain billions of parameters, which are learnable settings that adjust during training. GPT-3, released in 2020, had 175 billion parameters. More recent models are larger still, with some containing over a trillion parameters. As of late 2025, ChatGPT alone has over 800 million weekly active users worldwide, according to OpenAI.
You’ve likely encountered LLMs without realising it.
ChatGPT, Claude, Google Gemini, and Microsoft Copilot all run on large language models. When Google shows you an AI Overview at the top of search results, that’s an LLM generating the response. When Perplexity summarises information from across the web, an LLM creates that summary.
How LLMs Actually Work
How Do LLMs Predict the Next Word?
At their core, LLMs do one thing exceptionally well: they predict what word should come next.
Given some text, the model calculates probabilities for every possible next word and picks the most likely one. Then it adds that word and repeats the process.
Imagine the sentence: “The cat sat on the…” Your brain immediately suggests words like “mat”, “floor”, or “sofa”. You’ve learned from years of reading that certain words commonly follow others. LLMs work the same way, except they’ve been trained on trillions of words from books, websites, articles, and other text sources.
This training produces remarkably fluent text. But here’s the key insight: the model isn’t thinking about meaning. It’s recognising statistical patterns in language and reproducing them.
How Do LLMs Convert Text Into Numbers?
Computers only work with numbers. Before an LLM can process your question, it converts the text into numerical form.
First, the text gets broken into smaller pieces called tokens. A token might be a whole word, part of a word, or a punctuation mark. The word “understanding” might become two tokens:
“understand” and “ing”. This approach helps the model handle unfamiliar words by breaking them into recognisable components.
Each token then becomes a list of numbers called an embedding. These embeddings capture relationships between words.
Words with similar meanings end up with similar numbers, which helps the model grasp context. The word “king” might have embeddings close to “queen” and “royal”, while “banana” would be numerically distant from all three.
What Technology Powers Large Language Models?
The sophistication of modern AI assistants comes from two key innovations: transformer architecture and the attention mechanism.
What Are Transformers and the Attention Mechanism?
The breakthrough that made modern LLMs possible came in 2017 with transformer architecture, introduced in the research paper “Attention Is All You Need” by Google researchers.
Before transformers, AI systems processed text one word at a time, making them slow and limited in how much context they could consider.
Transformers introduced an attention mechanism. This allows the model to consider all words in a sentence simultaneously and work out which ones relate to each other. When processing “The bank was closed because it was a holiday”, the attention mechanism helps the model understand that “bank” refers to a financial institution (because of “closed” and “holiday”) rather than a riverbank.
Think about how you read a long email. You don’t give equal weight to every word. Your brain focuses on the important parts and skims the rest.
The attention mechanism works similarly, assigning different levels of importance to different words based on context.
How Much Data and Computing Power Do LLMs Need?
Creating an LLM requires enormous amounts of text data and computing power.
Training involves showing the model billions of text samples and adjusting its parameters to improve next-word prediction. When the model makes a wrong prediction, the parameters shift slightly. Over billions of examples, patterns emerge.
The scale is staggering. Training GPT-3 was estimated to cost between $2-4 million in computing resources. Training GPT-4 reportedly cost over £80 million, with some estimates exceeding £150 million when including research and development.
This explains why only large organisations can build these models from scratch. However, any business can use them through services like ChatGPT, Claude, or the APIs these companies provide.
What Are the Limitations of Large Language Models?
Understanding the limitations matters as much as knowing the capabilities.
An LLM doesn’t know that Paris is the capital of France the way you do.
It has learned patterns in how these concepts relate across millions of text examples, encoding this as mathematical relationships in its parameters. Whether this constitutes ‘understanding’ remains debated, but it’s more than simple word association.
This leads to a well-known problem called hallucination. LLMs can confidently generate information that sounds plausible but is completely wrong. Research by AIMultiple found that even the latest models have hallucination rates exceeding 15% when analysing provided statements. They might cite sources that don’t exist or fabricate statistics.
The LLM text flows smoothly because the model excels at producing natural-sounding language, even when the content is invented.
LLMs also inherit biases from their training data. If the text they learned from contains stereotypes or outdated information, those patterns are likely to appear in their outputs.
Human oversight remains essential when using AI-generated content for your business.
Why Should Business Owners Understand How LLMs Work?
AI search tools like ChatGPT, Perplexity, and Google’s AI Overviews use LLMs to generate answers. With ChatGPT now processing over 1 billion queries per day, understanding how these systems select and cite information has become essential for online visibility.
When someone asks these tools for a recommendation, the response comes from an LLM predicting what helpful text should follow the question. If your website content aligns with how these models work, you’re more likely to be referenced in AI-generated answers.
This is the foundation of AI search optimisation, sometimes called Generative Engine Optimisation (GEO) or Answer Engine Optimisation (AEO).
Understanding that LLMs work through next-word prediction helps you create content that’s more likely to be picked up and cited by AI systems.
Clear, well-structured content with direct answers to common questions gives LLMs exactly what they need to reference your business. Vague, jargon-heavy text that circles around topics makes it harder for AI systems to extract useful information.
How Can You Apply This Understanding of LLMs?
Large language models are remarkable technology, but they’re not magic.
They’re sophisticated systems that predict the most likely next word based on patterns learned from training data. Understanding this simple truth helps you use these tools more effectively and create content that AI search engines can find and cite.
The key points to remember: LLMs generate text by predicting what comes next, they don’t truly understand meaning, and they can make confident-sounding mistakes. With this knowledge, you’re better equipped to work with AI tools and adapt your online presence for a world where AI increasingly shapes how people discover information.
If you want your business to appear when people ask AI tools for recommendations, read our complete guide to AI search optimisation, which covers the specific content strategies that help AI systems find and cite your website.
Frequently Asked Questions
LLM stands for Large Language Model. The “large” refers to the billions of parameters these AI systems contain. Language models are designed to process and generate human language, and the largest ones power tools like ChatGPT, Claude, and Google’s AI features.
A chatbot is an interface for conversing with software. An LLM is the technology powering the responses. Think of the chatbot as the shopfront and the LLM as the expertise behind the counter. Not all chatbots use LLMs, but the most capable ones do.
No. LLMs recognise patterns in text and predict what words should come next. While the results often seem intelligent, the model has no genuine comprehension or awareness. It produces statistically likely text, not considered responses.
Tokens are the small pieces LLMs break text into before processing. A token might be a whole word, part of a word, or punctuation. The word “running” might become “run” and “ning”. This helps models handle the variety of possible words and phrases efficiently.
LLMs predict text based on patterns, not facts. They generate whatever sequence seems most probable, regardless of truth. This is called hallucination. Research shows hallucination rates of 15-40% depending on the task and model. The model might confidently cite non-existent studies because the text sounds plausible based on its training.
Training takes months using thousands of specialised chips working simultaneously. The exact time depends on model size and training data volume. The process costs millions of pounds, which is why only large organisations train models from scratch.
AI search tools use LLMs to generate answers. If your content is clear, well-structured, and directly answers questions, it’s more likely to be referenced in AI-generated responses. This matters increasingly as more people use AI tools instead of clicking traditional search results.
The attention mechanism helps an LLM work out which words in a sentence relate to each other. When processing “She put the apple in the basket because it was empty”, attention helps the model understand “it” refers to “basket”, not “apple”.
Most modern AI writing tools use LLMs, but specific models vary. ChatGPT uses GPT models, Claude uses Anthropic’s models, and Google’s tools use Gemini. Some simpler tools use smaller or older technology. Output quality often reflects which model powers the tool.
LLMs assist with writing but don’t replace human judgement and creativity. They can draft content, suggest ideas, and speed up repetitive tasks. However, they need human oversight to check facts, maintain brand voice, and ensure content genuinely serves your audience.