How Large Language Models Work: A Plain-English Guide

Large language models—LLMs—are the technology behind ChatGPT, Claude, Gemini, and most of the AI tools you use today. They generate text, answer questions, write code, summarize documents, and carry on extended conversations. But how do they actually work?

The honest answer involves a surprising amount of statistics and very little human-like understanding in the sense we typically mean that phrase. Understanding how LLMs work helps you use them better and have calibrated expectations about when they'll help and when they'll mislead you.

The Basic Idea: Predicting the Next Word

At the most fundamental level, a large language model is trained to predict what comes next in a piece of text.

Given the words "The cat sat on the," a well-trained model should assign high probability to "mat", "floor", "chair"—and low probability to "gravity" or "Tuesday." Do this prediction task well, across billions of examples, and something remarkable emerges: the model learns grammar, facts, reasoning patterns, and much more as a side effect of getting good at prediction.

This is counterintuitive. No one told the model what grammar is. No one gave it a list of facts. It inferred all of it from patterns in text because those patterns are what you need to predict text accurately.

The training data is the key input. Modern LLMs are trained on enormous text datasets—billions of documents from the web, books, code repositories, academic papers, and more. The scale is hard to grasp: GPT-4's training data, if printed, would fill millions of books.

The Transformer Architecture

The specific model architecture that made large language models possible is called the transformer, introduced in a landmark 2017 paper ("Attention Is All You Need").

Before transformers, neural networks processed text sequentially—word by word—which created problems with long-range dependencies. (Connecting "The trophy didn't fit in the suitcase because it was too large" requires understanding that "it" refers to the trophy, not the suitcase—a connection that spans eight words.)

Transformers solve this with a mechanism called attention. Attention allows the model to look at all parts of the input simultaneously and learn which parts are most relevant to each other. When processing the word "it" in that sentence, the attention mechanism can consider "trophy" and "suitcase" simultaneously and assign relevance based on learned patterns.

The model has many attention heads—different attention mechanisms that each learn to focus on different types of relationships. One head might specialize in grammatical dependencies; another might track thematic coherence; another might handle factual associations.

This parallel processing is also why transformers scale so well on modern hardware. GPUs and TPUs are designed to do many matrix multiplications simultaneously, which is precisely what the attention mechanism requires.

Training: Where the Learning Happens

Training a large language model requires three main phases in most modern systems:

Pretraining is the primary training phase. The model processes enormous amounts of text and adjusts its parameters—billions of numerical values—to improve at the next-word prediction task. This phase requires massive compute: training a frontier model costs tens of millions of dollars in GPU time alone.

After pretraining, the model is good at predicting text but not necessarily good at following instructions or being helpful. It might complete a question with more questions rather than an answer, because that's what often follows questions in text.

Supervised fine-tuning exposes the model to examples of desirable behavior—questions paired with good answers, instructions paired with appropriate responses. This shapes the model toward being useful.

Reinforcement learning from human feedback (RLHF) is a third phase used by most frontier labs. Human raters compare pairs of model outputs and indicate which is better. These preferences train a reward model, which then provides feedback signals that push the LLM toward responses humans prefer—more helpful, less harmful, more accurate.

The combination of pretraining scale, fine-tuning, and RLHF is what produces the capable, instruction-following models you interact with through consumer products.

What's Actually Inside the Model

After training, an LLM is essentially a very large function: text goes in, text comes out. What's inside is billions of numerical parameters organized in layers of matrix multiplications.

There's no explicit knowledge base. No database of facts. No explicit grammar rules. No reasoning engine. The model's apparent knowledge is distributed across the parameters as statistical associations, learned from patterns in training data.

This is why LLMs can sound authoritative about things they're wrong about. When the model generates a false statement, it's not lying—it's producing text that looks statistically similar to what correct statements look like in its training distribution. The model has no internal mechanism that checks statements against reality.

It's also why context matters so much. When you provide relevant information in a prompt, the model's attention mechanisms can use that information directly. The model is better at tasks where the answer is present in the context than at tasks that require recall of specific facts from training.

Parameters and Scale

When you hear that a model has "70 billion parameters" or "405 billion parameters," those numbers refer to the numerical values the model learned during training. More parameters allow a model to represent more complex patterns—but only if trained on sufficient data with sufficient compute.

The relationship between model size, data volume, and compute is described by scaling laws. Roughly speaking, you get the best results from a given compute budget by scaling model size and data volume together. Much of the dramatic quality improvement in AI systems over the past several years reflects simply training larger models on more data.

Beyond a certain scale, qualitative capabilities emerge that smaller models don't exhibit. Reasoning through multi-step problems, following complex instructions, and generating long-form coherent content all improve substantially with scale in ways that aren't well understood theoretically.

Why LLMs Hallucinate

"Hallucination"—generating confident-sounding false statements—is one of the most important limitations of current LLMs to understand.

The model generates each token (roughly a word or word fragment) by sampling from a probability distribution over the vocabulary. It doesn't have a "check for accuracy" step. It produces text that fits the statistical pattern of the surrounding context, and sometimes that text is factually wrong.

Hallucination is more common in certain situations:

• Facts that appear rarely in training data (obscure people, specific dates, niche technical details)
• Logical reasoning chains longer than the model can handle reliably
• Requests to produce citations or URLs, which the model generates by pattern-matching to what citation-looking text looks like
• Confidently stated claims in domains where the model has limited training examples

The practical response is to treat LLM-generated factual claims as drafts that require verification, not as reliable sources. The model is excellent at structuring and generating text; it is not reliable as a primary source for facts.

Retrieval-Augmented Generation

One approach to the hallucination problem is retrieval-augmented generation (RAG), where the model is given access to a document store and retrieves relevant documents before generating a response.

Rather than relying on facts baked into model parameters during training, the model can reference current, specific documents. This reduces hallucination on factual questions and allows the model to work with information that wasn't in its training data.

RAG is now standard in enterprise AI applications where factual accuracy matters. See Retrieval-Augmented Generation for a deeper look at how it works.

What Comes After LLMs

Current frontier systems layer additional capabilities on top of the base language model:

Tool use: Models can call external tools—search engines, calculators, code interpreters, APIs—and incorporate the results into their responses. This addresses some factual accuracy limitations by offloading certain queries to reliable sources.

Long context: Context windows have grown from a few thousand tokens to hundreds of thousands in recent years, allowing models to process and reason over much longer documents.

Multimodal input: Modern systems process images, audio, and video in addition to text, which opens up new application categories.

The underlying architecture is still largely transformer-based, though researchers are actively exploring alternatives. Whatever succeeds the transformer, the core insight—learning rich representations from the statistical structure of data at scale—seems likely to persist.

For a look at how these models are being compared and evaluated today, see AI Benchmarks and Next-Generation AI Models.