What is a large language model?

A large language model, usually shortened to LLM, is a type of AI that can read, write, summarise, translate, and reason about text. ChatGPT, Claude, Gemini and Grok are all built on LLMs. So is the AI that drafts your emails, the chatbot on your bank's website, and the search assistant that now appears above your Google results.

The name sounds technical, but it breaks down simply. Language model means a system that works with language. Large means it was trained on an enormous amount of text and has an enormous number of internal settings. That combination of scale is what makes modern LLMs surprisingly capable compared to anything that came before.

This article explains what an LLM actually is, how it learns, what it is doing when it answers you, and where it falls short. No maths required.

The core idea: predicting the next word

At its heart, a large language model uses patterns and relationships in language to predict the likelihood of a next word based on the words that came before it. That sounds too simple to be impressive, but the results are remarkable when you do it at scale.

Think of it like this. If someone says to you "the sky is", you immediately think "blue". You know that because you have read and heard millions of sentences in your lifetime. An LLM does something structurally similar, except instead of a lifetime of human experience, it has been exposed to a significant fraction of the written internet, plus books, academic papers, and more.

An LLM learns by predicting the next token over and over, billions of times. If the prediction is wrong, it adjusts its internal weights. If the prediction is right, it reinforces its understanding. A "token" here is not quite a word. LLMs do not read words the way humans do. They read tokens, which are small chunks of text, sometimes a single character, sometimes a full word. The word "picture" might be a single token; something rarer might get split into pieces.

The whole training process is essentially prediction plus correction, repeated at a scale that is hard to imagine. It is possible for a large language model to generate entire paragraphs or compositions of text in this manner.

What makes it "large"?

Two things make an LLM large: the amount of data it trains on, and the number of parameters it contains.

Many LLMs are trained on data gathered from the internet, thousands or millions of gigabytes' worth of text. That is before you count the books, scientific papers, and code repositories that are often included too.

Parameters are the model's internal settings, the numbers that get adjusted during training until the model becomes good at predicting language. Large language models have large numbers of parameters, akin to memories the model collects as it learns from training. OpenAI's GPT-3 model, for example, has 175 billion parameters. More recent models are thought to have more still, though the exact figures for the newest systems are rarely published.

The scale matters because language is complicated. Sarcasm, ambiguity, idiom, the difference between a question and a statement, the way meaning shifts depending on context: you need an enormous number of adjustable settings to capture all of that.

The transformer: the architecture underneath

Every modern LLM is built on a specific design called the transformer, which was invented by researchers at Google in 2017. You do not need to know how it works in detail, but one part of it is worth understanding: the attention mechanism.

An LLM is a type of neural network that specifically uses transformer architectures, which are models designed to detect dependencies between different parts of a sequence of data, regardless of their distance from each other.

What that means in practice is that when the model reads the sentence "The trophy did not fit in the suitcase because it was too big", the transformer can work out that "it" refers to the trophy and not the suitcase, even though those words are far apart. Earlier AI systems struggled with exactly this kind of long-range relationship. The transformer solved it.

Unlike earlier recurrent neural networks that sequentially process inputs, transformers process entire sequences in parallel. That parallelism is also why LLMs could be trained on the quantities of data they need. Processing a sentence word by word, in order, simply takes too long at the scale required.

Training versus using the model

It helps to separate two distinct phases: training and inference.

Training is when the model learns. It reads vast amounts of text, makes predictions, gets corrected, and slowly improves. This happens once (or a few times, with updates), costs enormous amounts of money and electricity, and takes weeks or months on thousands of specialist chips. The most popular LLMs are the result of immense amounts of data, GPUs, energy, and human expertise, which is why most are built and maintained by large tech companies with expansive resources.

Inference is when you use the model. You type a question, the model generates a response, token by token. Each token it produces gets fed back in as context for the next one, which is why the text appears word by word rather than all at once. This phase is much cheaper than training, though still not free, which is one reason AI companies spend so heavily on hardware.

An important implication of this: the model does not learn from your conversations in real time. Its knowledge was fixed when training ended. Large language models do not always generate exactly the same output when an identical input is provided multiple times. There is randomness baked into the generation process, which is why asking the same question twice can get you slightly different answers.

What an LLM can do

Large language models are incredibly flexible. One model can perform completely different tasks such as answering questions, summarising documents, translating languages, and completing sentences. This versatility is one of the things that surprised even the researchers who built them. Earlier AI systems were narrow: one system for translation, a different one for summarisation, another for question answering. An LLM does all of them.

LLMs can solve maths problems, plan multi-step processes, and explain complex concepts in simpler terms. They can write code, spot errors in contracts, generate marketing copy, and answer medical questions (though the last two deserve caution). The same underlying system does all of this because language itself is remarkably general. If you can express a task in words, an LLM has at least a chance of helping.

Developers can use pretrained models to build chatbots, knowledge retrieval systems, automation tools, and more. Most of the AI products you encounter day-to-day are not raw LLMs. They are applications built on top of one, with extra instructions, guardrails, and access to specific data sources layered on.

Where LLMs go wrong

Understanding the weaknesses is just as important as understanding the strengths.

The most discussed problem is hallucination. During hallucinations, the model generates information that is false or misleading while sounding plausible. This happens because the model is always doing the same thing: predicting what text should come next. If its training data did not cover a topic well, or if the correct answer is genuinely hard to predict from context, it will still produce something fluent, even if that something is wrong.

LLMs can also reflect and amplify biases present in their training data, producing outputs that are unfair or offensive. If the text the model trained on over-represents certain viewpoints or under-represents certain groups, those patterns get baked into the model's behaviour.

There are also practical limits around knowledge. The domain of an LLM may not include a specific factual answer. In some cases it may assemble a coherent answer, but a fact stated in the answer may be incorrect. This is why using an LLM for anything where accuracy really matters, medical decisions, legal advice, financial planning, requires treating it as a starting point rather than a definitive source.

Finally, their resource demands are significant: training and running LLMs requires large amounts of computational power and energy, raising both cost and environmental concerns.

How LLMs differ from search engines

People often ask whether LLMs will replace search engines. They do something quite different. A search engine finds pages that probably contain an answer, then shows them to you. An LLM generates an answer directly, constructing it from what it learned during training rather than retrieving it from a live index of the web.

That difference has real consequences. A search engine can point you to a page published this morning. An LLM trained six months ago has no idea that page exists. On the other hand, an LLM can synthesise information from many sources into a single coherent answer, which a search engine cannot do on its own. Both approaches have genuine uses. Most of the big AI products are now trying to combine them by giving LLMs access to live search.

The models you have probably heard of

A few names come up repeatedly when people talk about LLMs.

• GPT-4 and GPT-4o (OpenAI): the models behind ChatGPT. Generative pretrained transformers are perhaps the best-known large language models, developed by OpenAI, with numbered iterations that are improvements on their predecessors.
• Claude (Anthropic): a family of models developed with a particular focus on safety and reducing harmful outputs.
• Gemini (Google DeepMind): Google's main LLM family, integrated into Search, Workspace, and Android.
• Llama (Meta): an open-weight model, meaning its parameters are publicly available for anyone to download and run. This makes it popular with researchers and developers who want to customise a model without paying per query.
• DeepSeek (DeepSeek AI): a Chinese-developed model that attracted significant attention in early 2025 by matching the performance of much more expensive Western models at a fraction of the reported training cost.

They all work on the same basic transformer principle. They differ in training data, parameter count, the extra steps taken after basic training (such as fine-tuning on human feedback), and the specific guardrails their developers have applied.

What happens after the initial training

Raw LLM training produces a model that is very good at completing text, but not necessarily good at following instructions or being helpful and safe. So most companies run an additional stage called fine-tuning, which involves showing the model examples of good and bad responses, often rated by human reviewers.

One common technique is called reinforcement learning from human feedback, or RLHF. Human raters compare different model responses and indicate which they prefer. That preference signal gets used to nudge the model's behaviour in a more useful and less harmful direction. This is roughly what separates the raw "complete this text" capability from the polished assistant behaviour you see in ChatGPT or Claude.

The model you actually use is the result of all these layers: pre-training on vast text, then fine-tuning on curated examples, then alignment work to reduce harmful outputs, then whatever additional customisation the product team has applied on top.

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References

1. What is a large language model (LLM)?, Cloudflare Learning Centre
2. What is LLM? Large Language Models Explained, AWS
3. What Are Large Language Models (LLMs)?, IBM Think
4. Understanding large language models: A comprehensive guide, Elastic
5. Large language models, explained with a minimum of math and jargon, Understanding AI
6. Training open-weight models is becoming more data intensive, Epoch AI
7. How LLM Training Actually Works, Medium