Large language models (LLMs) are the AI systems behind tools like ChatGPT, Claude, and Gemini, trained on vast amounts of text to predict and generate human-like language. Understanding roughly how they work, without needing a technical background, helps explain both what these tools are genuinely good at and where their well documented limitations, such as hallucination, come from.
In short: an LLM reads an enormous library of text, learns which words tend to follow other words, and then writes by guessing the most likely next word, one word at a time. Almost everything clever it does grows out of that single trick.
How Large Language Models Actually Work
At a basic level, an LLM is trained by processing enormous amounts of text and learning statistical patterns in how words and phrases relate to one another. When you give it a prompt, it does not look up a fact in a database, it predicts the most statistically likely sequence of words to follow, based on everything it learned during training. This is why LLMs are so fluent at generating natural sounding text, and also why they can confidently produce incorrect information, since fluency and factual accuracy are not the same thing to the underlying model.
A useful everyday analogy is your phone keyboard’s autocomplete, except far more sophisticated. Autocomplete guesses the next word from a few letters you typed. An LLM guesses the next word from thousands of words of context, using patterns learned from billions of sentences. That leap in scale is what turns a simple guessing game into something that can draft essays, explain code, and hold a conversation.
Breaking Text into Tokens
Before a model can read anything, text is chopped into small pieces called tokens. A token might be a whole short word like “cat”, or part of a longer word like “un” and “believable”. On average, one token is roughly three quarters of an English word, so a 750 word page becomes about 1,000 tokens. Models think in tokens, not words, which is why you sometimes see them stumble on tasks like counting letters in a word: they never actually saw the letters, only the token.
The Transformer: A Pattern Spotting Machine
Almost every modern LLM is built on an architecture called the transformer, introduced in a 2017 research paper. Its key skill is attention: when deciding the next word, the model weighs up which earlier words matter most. In the sentence “The animal didn’t cross the street because it was too tired”, attention helps the model link “it” to “animal” rather than “street”. Stack dozens of these attention layers on top of each other, give the model billions of adjustable settings called parameters, and it becomes remarkably good at tracking meaning across long passages.
How an LLM Is Trained: Three Stages
Training happens in stages, each one building on the last:
| Stage | What happens | What it produces |
|---|---|---|
| 1. Pre-training | The model reads a huge mix of books, websites, and articles, learning to predict the next word. This takes weeks or months on thousands of specialised chips. | A base model that knows a lot about language but is not yet a helpful assistant. |
| 2. Fine-tuning | The model is trained on thousands of example conversations showing good answers to real questions. | A model that responds helpfully and follows instructions. |
| 3. Human feedback (RLHF) | People rank the model’s answers, and it learns which responses humans prefer: accurate, safe, and well structured. | The polished chatbot experience you actually use. |
Key Capabilities of Modern LLMs
- Natural language understanding: Interpreting requests written in plain, conversational English rather than rigid commands.
- Text generation: Drafting emails, articles, code, and other written content in a requested style or format.
- Summarisation: Condensing long documents into shorter, readable summaries.
- Reasoning through multi-step problems: Working through problems that require several logical steps, though reliability varies by task complexity.
- Translation: Converting text between languages, often well enough for everyday use.
- Code assistance: Explaining, writing, and debugging code across most popular programming languages.
- Tool use: Modern LLMs can be connected to web search, calculators, and other tools to extend their capabilities beyond their trained knowledge.
Where LLMs Shine and Where They Struggle
| Strong suits | Weak spots |
|---|---|
| Drafting and editing text | Precise facts, dates, and figures |
| Summarising long documents | Very recent events without web access |
| Brainstorming ideas and outlines | Complex arithmetic done from memory |
| Explaining difficult topics simply | Keeping long stories fully consistent |
| Writing and debugging code | Tasks needing real world actions |
Common Limitations to Understand
- Knowledge cutoffs: Most models have a training cutoff date, meaning they may not know about very recent events unless connected to live search.
- Hallucination risk: Models can generate plausible sounding but false information, particularly on niche or highly specific topics. Because they predict likely text rather than retrieving facts, a confident tone is no guarantee of truth.
- No true understanding: LLMs predict patterns in text rather than possessing genuine comprehension, which explains occasional strange or inconsistent outputs.
- Sensitivity to prompt phrasing: How a question is asked can meaningfully affect the quality of the answer received.
- Bias in training data: Models absorb the biases present in the text they were trained on, so their answers can reflect stereotypes or skewed perspectives.
Getting Better Answers from Any LLM
A few simple habits make a noticeable difference. Be specific about what you want, including the format and length. Give the model context: paste in the relevant background rather than assuming it knows your situation. Ask it to show its reasoning step by step for tricky problems, which usually improves accuracy. And for anything important, verify key facts against a trusted source instead of taking the answer at face value.
Frequently Asked Questions
Are all AI chatbots built on the same technology?
Most modern AI chatbots are built on large language models, though the specific model, training data, and additional features vary between providers.
Do larger models always perform better?
Not necessarily. Model size is one factor among several, including training data quality and fine tuning, that affect real world performance on specific tasks. Smaller models are often cheaper and faster while being good enough for everyday jobs.
Can LLMs learn from my conversations with them?
This depends on the specific provider’s settings and policies, so it’s worth checking the privacy settings of whichever tool you’re using. Many providers let you opt out of having your chats used for training.
Will LLMs replace search engines?
Probably not entirely. Search engines point you to sources you can check; LLMs give you a direct answer you cannot easily verify. The two complement each other, and many tools now combine both: an LLM answer backed by live search results.
Can I trust an LLM with medical or legal advice?
Treat it as a starting point, not an authority. LLMs can explain general concepts clearly, but for decisions affecting your health, money, or legal rights, always confirm with a qualified professional.
For related reading, see our comparison of ChatGPT vs Meta AI and our guide to AI hallucinations.
