How Large Language Models Like GPT Actually Work

Large language models feel like magic when you first use one — you type a question, and something that reads like real understanding comes back. Once you know roughly what's happening underneath, the magic goes away, but something more useful replaces it: a sense of what these systems are actually good at, and where they'll quietly let you down.

The core trick: predicting the next word

At its core, a large language model does one thing: given a sequence of text, it predicts what word (technically, a "token" — often a word or part of a word) is most likely to come next. That's it. Generate one word, add it to the sequence, predict the next one, repeat. A whole essay comes out of a model doing this same narrow prediction task over and over, thousands of times per response.

What makes this convincing is scale. These models were trained on enormous amounts of text — books, articles, code, conversations — and adjusted their internal parameters until their predictions matched that text well. Do that at a big enough scale, and "predict the next word" turns out to be a genuinely powerful trick for producing text that reads as coherent, informed, and often accurate.

Where "understanding" actually comes from

The model doesn't have beliefs, facts stored in a lookup table, or a model of the world in the way a person does. What it has is a very refined sense of which words statistically tend to follow which other words, given everything it saw during training. When it correctly explains how a car engine works, it's not recalling a fact — it's producing the sequence of words that its training made most probable, which happens to line up with correct information because it saw a lot of accurate text about car engines.

This distinction matters because it explains both the strengths and the well-known failure mode.

Why these models sometimes make things up

"Hallucination" — a model confidently stating something false — isn't a bug in the sense of broken code. It's a direct consequence of how the system works. The model is always producing the most statistically plausible next word, whether or not that word corresponds to something true. If a topic is obscure, or a question has no clean factual answer in the training data, the model will still produce a fluent, confident-sounding response — because fluency is what it was optimized for, not truth. It doesn't have a way to check whether what it just said is real.

What this means practically

• These models are strong at language tasks — summarizing, rephrasing, drafting, translating, explaining a concept in a new way. The task is fundamentally about producing good text, which is exactly what they're built for.
• They're weaker as a source of facts, especially for anything specific, recent, or niche. Ask a factual question with a clean, verifiable answer, and you should verify it, not trust it outright.
• They don't know what they don't know. A model will answer a question it has no reliable basis for just as fluently as one it has excellent training data on. Confidence in the response isn't a signal of accuracy.
• Grounding fixes a lot of this. Feeding the model your own verified documents at the moment it answers — instead of relying purely on what it memorized during training — sharply reduces made-up answers, because the model is now working from real source text instead of guessing.

The practical takeaway for building a product

A large language model is an extremely capable text engine, not an oracle. Product features that lean on its language ability — drafting, summarizing, rewriting, structuring — tend to work great. Features that assume it "knows" your data, your policies, or your customers need to be built with grounding, guardrails, and a clear plan for what happens when it's wrong, not just wired up to the model and shipped.

That grounding-and-guardrails work is most of what goes into building an AI feature that actually holds up in production — at Burncode, it's the part of the project we spend the most time on, well after the initial demo has already impressed everyone in the room.