How Large Language Models Actually Work: ChatGPT And Modern AI Explained Simply

The Simple Science Behind ChatGPT And Large Language Models

What Actually Happens When You Ask ChatGPT A Question?

ChatGPT can write computer code, explain quantum mechanics, analyse documents, translate languages, generate stories and hold conversations that sometimes feel remarkably human. Yet underneath all of that apparent intelligence sits an idea that sounds almost absurdly simple: predict what should come next.

Large language models, or LLMs, learn patterns from enormous quantities of information and use those patterns to generate sequences of tokens. Modern systems have become so good at doing this that next-token prediction can produce behaviours that look like reasoning, explanation, creativity and problem-solving. OpenAI describes its foundation models as learning relationships within large amounts of information and using those learned patterns when generating responses.

The Simplest Way To Understand An LLM

Imagine reading the sentence: “The capital of France is…”

You almost certainly expect the next word to be “Paris”. You can make that prediction because you have encountered enough language and acquired enough knowledge to understand the relationship between France, capitals and Paris.

A language model performs something loosely analogous, except on an extraordinary numerical scale. Give it a sequence of text and it calculates probabilities for what could reasonably come next. It selects a token, adds that token to the existing sequence, then repeats the calculation again.

That means a long answer is not normally written internally as a complete paragraph and then revealed to you. It is generated incrementally. One token becomes part of the context used to predict the next token, which becomes part of the context for the token after that.

This sounds far too primitive to explain modern AI. The important part is what happens before the prediction.

Everything Starts With Tokens

Large language models do not initially see your sentence in quite the way you do.

Text is broken into units known as tokens. A token might be an entire word, part of a word, punctuation or even something as small as an individual character depending on the model and language.

OpenAI gives a rough rule for English that one token corresponds to about four characters or roughly three-quarters of a word, although this varies substantially. A 1,500-word document, for example, might contain somewhere around 2,000 tokens.

Suppose you type:

“Why is the sky blue?”

The model converts that text into a sequence of token IDs. Those IDs are numbers representing entries in its vocabulary.

At this stage, however, the computer still does not understand that “sky” is connected with atmosphere, sunlight, colour and Earth. A token ID is merely an identifier.

Something else has to turn those identifiers into useful mathematical representations.

How Words Become Numbers

Each token is mapped into what is called an embedding.

An embedding is essentially a long list of numbers representing information the model can work with mathematically. During training, useful relationships emerge in this numerical space.

Words and concepts that appear in related contexts can acquire related representations. The system therefore does not need a human programmer to manually write rules saying that “king”, “queen”, “monarch” and “royal” have connections.

Those relationships can be learned from the data.

But embeddings alone are not enough. Meaning depends heavily on context.

Consider the word “bank”.

“I deposited money in the bank.”

“We sat on the river bank.”

The letters are identical. The meanings are completely different.

A powerful language model therefore needs a mechanism capable of deciding which parts of a sentence matter to which other parts.

That mechanism is one of the breakthroughs that transformed modern AI.

Why The Transformer Changed AI

In 2017, researchers introduced a neural-network architecture called the Transformer in the landmark paper Attention Is All You Need.

Earlier language systems often processed sequences using recurrent neural networks that moved through information step by step. Transformers instead centred their architecture around a mechanism called attention, making training much more parallelisable while allowing the model to examine relationships across a sequence.

Almost every major modern LLM descends from this architecture in some form.

The GPT in ChatGPT stands for Generative Pre-trained Transformer.

Generative means it generates content.

Pre-trained means that before you ever speak to it, the underlying model has already undergone extensive training.

Transformer describes the basic neural-network architecture behind the original GPT family.

Understanding the Transformer therefore gets much closer to understanding why modern AI suddenly became so capable.

What Attention Actually Does

Attention sounds technical, but the basic idea is intuitive.

When humans read a sentence, different words matter depending on what we are trying to understand. Consider:

“The dog chased the ball because it was moving.”

To interpret “it”, you need to determine what earlier information the word most likely refers to.

An attention mechanism lets a model calculate relationships between tokens and decide which other tokens deserve greater weight while processing the current one.

Internally, Transformer attention uses mathematical representations commonly described as queries, keys and values. A simplified analogy is that the model asks what information the current token is looking for, compares that request against information associated with other tokens, and then combines the relevant information.

Real Transformers perform this repeatedly through multiple attention heads. Different heads can learn to track different useful relationships.

One might become sensitive to grammatical structures. Another may capture longer-distance relationships. Others can contribute to patterns involving concepts, entities or contextual associations.

Nobody manually programs an attention head with a rule saying, “you are responsible for pronouns”. Useful internal behaviours emerge through training.

That distinction is fundamental to understanding artificial intelligence and how machine learning differs from ordinary software. Traditional software is dominated by rules written explicitly by programmers. Machine-learning systems instead learn large numbers of internal numerical settings from examples.

What Billions Of Parameters Are Really Doing

Those numerical settings are called parameters.

When people say an AI model has billions of parameters, they are describing billions of adjustable numerical values spread throughout the neural network.

Parameters influence how information flowing through the model is transformed.

They are not billions of individually stored facts. Parameter number is therefore not the equivalent of counting entries in an encyclopaedia.

A useful analogy is an enormous control panel containing billions of tiny dials.

At the beginning of training, those dials are largely useless. The model produces bad predictions.

Training repeatedly adjusts them.

Across enormous numbers of examples, the network gradually becomes better at modelling the patterns contained in language and other training data.

The resulting parameter configuration can encode remarkably complicated statistical relationships between language, concepts and patterns without anyone explicitly specifying where an individual fact should be stored.

This is one reason LLMs are fundamentally different from databases.

A database might contain a row stating that Paris is the capital of France.

An LLM instead contains a huge distributed mathematical structure whose learned parameters make “Paris” overwhelmingly plausible in the right context.

How An LLM Learns During Training

The heart of pre-training is repetition on an almost unimaginable scale.

The model receives text and tries to predict the next token. Its prediction is compared with the actual token. The difference becomes an error signal.

An optimisation process then adjusts the model's parameters slightly so that better predictions become more likely in the future.

Repeat this process across vast datasets and enormous amounts of computing power and something extraordinary happens.

The model begins learning grammar because grammar helps predict language.

It learns facts because facts help predict language.

It learns associations between concepts because those associations improve predictions.

It learns styles, structures, relationships, patterns in computer code, common forms of reasoning and countless other regularities because all of them help reduce prediction error.

OpenAI's GPT-4 technical report describes its base model as a Transformer-based model pre-trained to predict the next token in a document. Earlier GPT-3 research also demonstrated that scaling an autoregressive language model dramatically improved its ability to perform tasks from instructions and examples supplied entirely through text.

The remarkable part is that nobody needs to create a separate fundamental model for translation, summarisation, answering questions and writing code. Many of those capabilities can emerge inside one general model because all can be represented as sequences.

What Happens When You Type A Prompt

Now imagine asking ChatGPT:

“Explain gravity as if I am ten.”

First, your message and other relevant instructions or conversational context are transformed into tokens.

Those tokens become numerical representations and pass through the model's Transformer layers. Attention mechanisms repeatedly calculate relationships between different pieces of the context while the network transforms the information through layer after layer.

Eventually, the model produces a set of scores for possible next tokens.

These scores can be converted into probabilities.

Perhaps several beginnings are plausible:

“Gravity…”

“Imagine…”

“Think…”

“You…”

The system selects an output according to its generation process. That new token is appended to the sequence.

Then the whole process continues.

Token after token, the answer appears.

This is also why AI responses can stream onto a screen while they are being produced rather than appearing only after an entire answer has been completed.

Why Next-Token Prediction Becomes So Powerful

This is where descriptions of LLMs often become misleading.

Calling one “just autocomplete” is technically suggestive but conceptually inadequate.

Ordinary autocomplete might predict the next few words from shallow patterns. A frontier LLM can use an enormous neural network to condition its predictions on a large context while drawing on complex representations learned during training.

Predicting language extremely well requires modelling some of the structures that produced language in the first place.

If a model has to predict the next token in a physics explanation, understanding patterns related to physics improves its prediction.

If it predicts the next line of software, patterns related to programming help.

If it completes a logical argument, relationships associated with reasoning become useful.

That does not prove the model thinks exactly as a human thinks. It explains why a seemingly modest training objective can produce capabilities far beyond sentence completion.

The emergence of this broader capability was already visible with GPT-3. Researchers found that increasing model scale substantially improved zero-shot and few-shot task performance, allowing one model to perform many tasks without conventional task-specific retraining.

Why ChatGPT Is More Than A Raw Language Model

A raw pre-trained model is not automatically a useful assistant.

It has learnt to continue sequences, but a user wants something more specific: follow instructions, answer questions, avoid certain harmful behaviours, maintain conversational structure and produce useful responses.

This is where post-training becomes important.

When ChatGPT was introduced in 2022, OpenAI described using supervised fine-tuning followed by reinforcement learning from human feedback. Human trainers produced conversations, ranked alternative model responses and helped create signals that encouraged more useful behaviour.

Modern post-training systems have evolved considerably, but the distinction remains important.

Pre-training creates much of the underlying capability.

Post-training shapes how that capability behaves.

And the product around the model can add another layer entirely: search, uploaded documents, memory, calculators, computer tools, code execution or other systems.

This is why the move from ordinary chatbots towards AI agents matters. Once a language model can use external tools, it is no longer restricted to generating an answer. It can potentially retrieve information and take sequences of actions.

Why LLMs Hallucinate

The architecture also explains one of modern AI's biggest weaknesses.

Language models are fundamentally optimised to produce plausible continuations. Plausibility and truth are not identical.

Imagine asking about an obscure event that never occurred.

The linguistic pattern of your question may strongly suggest that an answer should contain a date, location, explanation and names. Unless the system successfully recognises the false premise or verifies the information elsewhere, generating a convincing fictional answer may fit the learned pattern better than admitting uncertainty.

This produces what is commonly called a hallucination.

Modern AI systems can reduce this problem through better training, reasoning techniques, retrieval, web search, tools, verification and improved post-training. They cannot simply make the underlying distinction between fluent language and guaranteed truth disappear.

That is why an LLM should not be imagined as an omniscient encyclopaedia.

It is a learned generative model.

Does ChatGPT Actually Understand Anything?

This becomes partly a technical question and partly a philosophical one.

Clearly, an LLM does not understand the world exactly as a human does. Humans have bodies, senses, biological drives, childhood experiences, continuous memories and direct interaction with a physical environment.

A language model processes mathematical representations.

Yet saying it therefore has “no understanding whatsoever” creates another problem. Modern models can manipulate concepts, solve unfamiliar problems, translate between representations, follow new instructions and generalise patterns to situations that were not written explicitly into their programming.

The more useful question may therefore be: what kind of internal representations are required to produce these capabilities?

Scientists continue trying to answer that.

We understand the broad architecture and training objective extremely well. What is far harder is explaining exactly how billions of interacting parameters represent individual concepts or how sophisticated behaviours emerge across large networks.

In that sense, engineers know how to build the machine more clearly than they know how to interpret every process occurring inside it.

Where Large Language Models Go Next

Large language models are already becoming only one component of larger AI systems.

Models can be connected with search engines, software tools, databases, memory, vision, audio, computer interfaces and external environments. A model can increasingly become the reasoning and communication layer inside a system that does far more than generate text.

That leads towards the much larger debate around artificial general intelligence. Whether today's architectures eventually produce something that deserves that label remains unresolved, and intelligence itself has no universally accepted engineering threshold.

But the central idea behind today's revolution is no longer mysterious.

Your words are divided into tokens. Tokens become mathematical representations. Transformers use attention to relate pieces of information to one another. Billions of learned parameters transform those representations through multiple layers. Training makes the network increasingly good at predicting what comes next. Post-training turns that underlying capability into something closer to an assistant.

Then the model predicts one token.

And another.

And another.

From that deceptively simple loop has emerged one of the most consequential technologies of the modern era.