The Question Everyone Is Asking

In the last few years, AI language models have gone from a niche research curiosity to tools used by millions of people daily. Yet most people using them have only a vague sense of what's actually happening when they type a question and receive a fluent, detailed response.

This explainer cuts through the hype and the technical jargon to give you a genuine understanding of how these systems work.

Step 1: Training on Text

A large language model (LLM) begins its life by being trained on an enormous corpus of text — books, websites, articles, code, and more. During training, the model is given a simple but powerful task: predict the next word (or token) in a sequence.

Over billions of examples, the model adjusts its internal parameters — numerical values that determine how it processes language — to get better and better at this prediction task. By the end of training, the model has encoded statistical patterns about how words, ideas, and concepts relate to each other across an extraordinarily wide range of topics.

Step 2: Tokens, Not Words

LLMs don't actually process words — they process tokens, which are chunks of text that can be whole words, parts of words, or punctuation. The word "unbelievable" might be split into "un", "believ", and "able." This tokenization allows the model to handle rare words and languages more efficiently.

Step 3: Attention — The Core Innovation

The architecture that powers modern LLMs is called the Transformer, introduced in a landmark 2017 research paper. Its key innovation is a mechanism called attention.

Attention allows the model to weigh the relevance of every word in a sentence relative to every other word when producing its output. When processing the sentence "The trophy didn't fit in the suitcase because it was too big," attention helps the model determine that "it" refers to "the trophy," not "the suitcase" — a task that requires understanding context across the full sentence.

Step 4: Generating Responses

When you type a prompt, the model doesn't look up an answer in a database. Instead, it generates a response one token at a time, each time predicting the most probable (or contextually appropriate) next token given everything that came before.

A bit of randomness is typically introduced to prevent responses from being boringly repetitive — this is controlled by a parameter called "temperature." Higher temperature = more creative and varied. Lower temperature = more focused and predictable.

What LLMs Don't Do

  • They don't "understand" in the human sense. They model statistical patterns in language, which produces outputs that can appear deeply insightful — but the model has no internal experience or genuine comprehension.
  • They don't look things up in real time (unless given special tools to do so). Their knowledge is frozen at whatever date their training data was collected.
  • They can be confidently wrong. Because they generate the most statistically plausible-sounding text, they can produce fluent misinformation — a phenomenon called "hallucination."

Why This Matters

Understanding how LLMs work makes you a better, more critical user of them. You'll know why they sometimes confidently state incorrect facts, why giving them more context produces better results, and why they're genuinely remarkable at certain tasks while surprisingly bad at others.

These models are tools — extraordinarily powerful ones. Like all tools, they work best when the person using them understands what's under the hood.