How Does ChatGPT Work? Large Language Models Explained

ChatGPT is the most familiar example of a large language model, but it isn't a special case. Claude, Gemini, Llama, and ChatGPT all run on the same underlying idea: predict the next chunk of text, over and over, based on patterns learned from an enormous amount of writing.

That sounds too simple to explain something that can write essays and debug code, and the gap between that description and what these systems actually do is the interesting part.

This guide covers what a large language model is, what happens when you type a prompt, how these models are trained, and why they sometimes state false things with total confidence. Veritas AI pairs high school students with mentors, often PhDs or PhD candidates in machine learning, who work with these systems directly, which is a considerably faster way to understand them than reading about them.

Key Takeaways

• A large language model predicts the most likely next token, or chunk of text, one at a time, and ChatGPT is one product built on this.

• Text is broken into tokens rather than words, and each token is represented as a list of numbers.

• The transformer architecture, introduced in 2017, uses attention to weigh which earlier words matter most.

• Training happens in stages: pretraining on huge text collections, then fine-tuning with human feedback.

• Hallucinations happen because the model optimizes for plausible text, not for truth.

• The model has no memory between conversations and no built-in access to current information.

What Is a Large Language Model?

A large language model is a neural network trained to predict text, and ChatGPT is a chat interface built on top of one. The distinction matters because the model is the underlying system, and the chat product is just one way to interact with it. The same model can power a coding assistant, a search tool, or an API that other software calls.

They're called large for a literal reason. These models contain billions of adjustable numbers, called parameters, that training tunes. Those parameters encode the statistical patterns picked up from the training data, and they're what allow the model to produce fluent language rather than nonsense.

Every major system you've encountered works this way. The differences come from training data, model size, and fine-tuning choices rather than fundamentally different architectures.

How Does a Language Model Actually Generate Text?

At its core, the model does one thing repeatedly: given everything so far, predict what comes next. You type a question, and the model calculates probabilities across its entire vocabulary for what token should follow. It picks one, adds it to the sequence, then repeats the whole calculation with that new token included.

Every word you see appear was generated this way, one piece at a time, with each prediction depending on everything before it. The model isn't planning a complete answer and then writing it out. It makes a well-informed guess at each step.

A setting called temperature controls how much randomness enters this selection. Low temperature makes the model almost always pick the highest-probability token, producing consistent but repetitive output. Higher temperature lets it choose less likely options sometimes, which reads as more creative. This is also why asking the same question twice can produce different answers.

What Are Tokens?

Models don't work with words directly. Text gets broken into tokens, which can be a whole word, part of a word, or a punctuation mark. Common words are usually single tokens, while unusual words get split into pieces.

Each token is converted into a list of numbers called an embedding, which places it in a mathematical space where related meanings sit near each other. This is what lets the model treat "dog" and "puppy" as related without anyone programming that relationship in.

Tokens also explain some odd model behavior. Counting letters in a word is surprisingly hard for a system that never sees individual letters, only tokens, which is why models have historically struggled with questions like how many times a letter appears in a word.

What Is the Transformer Architecture?

The breakthrough that made modern language models possible came from a 2017 research paper called Attention Is All You Need, which introduced the transformer.

The key idea is the attention mechanism, which lets the model weigh how much every word in the input should influence its interpretation of every other word. Take the sentence "the trophy didn't fit in the suitcase because it was too large." Deciding whether "it" refers to the trophy or the suitcase requires connecting words that are far apart. Attention is what makes that possible.

Earlier approaches processed text strictly in order, which made long-range connections difficult and training slow. Transformers process the whole sequence at once and learn which relationships matter, making them more accurate and much easier to run on modern hardware. That parallelism is a large part of why these models scaled up so quickly.

How Are Large Language Models Trained?

Training happens in distinct stages, and the difference between them explains a lot about the model's behavior.

Pretraining is the first and by far the largest stage, where the model reads an enormous collection of text and repeatedly tries to predict the next token. This is self-supervised learning: nobody labeled the data, since the correct answer is simply the word that actually came next. Every wrong prediction nudges the parameters slightly, and after enough repetitions the model becomes very good at producing plausible text.

A pretrained model is fluent but not useful as an assistant. It will happily continue your question with more questions, since that's a statistically reasonable continuation.

Fine-tuning fixes that by training the model on examples of helpful responses, teaching it to answer rather than merely continue. A further stage called reinforcement learning from human feedback has people rank different responses, and the model is adjusted toward the ones humans preferred. This is what shapes the assistant behavior you actually interact with.

Why Do Language Models Make Things Up?

This is the most important thing to understand about these systems. The model was trained to produce likely text, not true text, and those two goals overlap most of the time but not always.

When you ask about something well represented in its training data, the most probable continuation usually is the correct one. When you ask about something obscure, the model still produces the most plausible-sounding answer it can, and plausible-sounding is not the same as accurate. A fabricated citation looks statistically identical to a real one.

The model also has no internal mechanism for checking whether it knows something. It doesn't experience uncertainty the way you do, which is why fabricated answers arrive in the same confident tone as correct ones. Treating confident phrasing as evidence of accuracy is the single most common mistake people make with these tools.

What Are the Limitations of Large Language Models?

The model has a context window, which is the maximum amount of text it can consider at once. Anything beyond that gets dropped, which is why very long conversations start losing earlier details.

It also has no memory between separate conversations unless a memory feature explicitly stores something, and its knowledge is frozen at the end of its training data unless it's given search tools to look things up.

Arithmetic and precise counting remain weak spots, since predicting plausible text is a poor way to calculate. Most products now route these to actual calculators rather than relying on the model. Whether these systems reason in any meaningful sense is still genuinely debated among researchers, which matters when you encounter confident claims in either direction.

How Can You Learn to Build With Language Models?

Understanding these systems well enough to build with them is a different skill from using them, and it's more accessible than most students assume. The transformer architecture is well documented, and you can implement and train small versions on a normal laptop.

A reasonable path starts with Python and basic machine learning, moves through neural network fundamentals, and then into transformers and attention specifically. Working with pretrained models through a library like Hugging Face lets you build real applications long before you could train something from scratch.

Reading about attention mechanisms and implementing one are different experiences, and the second is where the understanding actually settles. Veritas AI is built for that step. The AI Scholars program spends ten weeks in a small group covering machine learning fundamentals, Python, data analysis, and model evaluation, which is the foundation everything else builds on. The AI Fellowship pairs you one-on-one with a mentor for twelve to fifteen weeks to build an original applied AI research project, with support toward publication if the work merits it. Mentors are often PhDs or PhD candidates from schools like Harvard, Stanford, Yale, Oxford, Cornell, and Columbia who work with these architectures in their own research.

Resources

Foundational reading

• Attention Is All You Need: The original transformer paper that underpins modern language models.

• Google's Machine Learning Crash Course: Free structured introduction to the fundamentals.

• Hugging Face NLP Course: Free, practical course on transformers and language models.

Helpful tools

• Hugging Face Models: Pretrained models you can experiment with directly.

• Kaggle Learn: Short tutorials covering Python and machine learning basics.

Frequently Asked Questions About ChatGPT and Large Language Models

1. Are large language models actually intelligent?

It produces text that reads as intelligent without understanding meaning the way people do. Whether that constitutes intelligence is a genuine open debate among researchers, not a settled question in either direction.

2. Does ChatGPT search the internet for answers?

Not by default. The base model generates answers from patterns learned during training, though many products now add search tools that let the model look things up and cite sources. Without those tools, it's working purely from what it learned.

3. Why does ChatGPT give different answers to the same question?

A setting called temperature introduces controlled randomness into which token gets selected at each step, so the model doesn't always pick the single most probable option. This is what makes output feel varied rather than mechanical.

4. What does GPT stand for?

Generative Pre-trained Transformer. Generative means it produces new text, pre-trained refers to the initial large-scale training stage, and transformer is the underlying architecture. Other models use different names but the same core design.

5. Can ChatGPT learn from my conversations?

Not within a conversation in the way people learn. The model's parameters are fixed after training, so it isn't updating itself as you chat. It can reference earlier messages in the current conversation because they're included in its input, and some products store information across sessions as a separate feature.

6. What is the difference between ChatGPT and a large language model?

A large language model is the underlying neural network, while ChatGPT is a product built on top of one. The same kind of model can power a coding tool, a search feature, or an API. ChatGPT is simply the most widely used chat interface to one.

7. Are all AI chatbots built the same way?

The major ones share the same foundation. Claude, Gemini, Llama, and ChatGPT are all transformer-based language models trained to predict text, then fine-tuned to behave as assistants. Differences come from training data, scale, and fine-tuning choices, not fundamentally different architectures.

8. Why are language models bad at math?

They predict plausible text rather than compute results, so arithmetic is pattern-matched rather than calculated. Tokenization makes this worse, since numbers get split in ways that don't align with place value. Most products now hand calculations to actual computation tools.