We talk about artificial intelligence in the language of biology. An LLM has "neurons." It "learns." We "train" it, and when it gets something wrong we say it needs to "see more examples." The vocabulary makes it sound as though we've built a digital brain — that ChatGPT learns the way a child does, only faster.
It's a seductive idea, and it's mostly wrong. The words rhyme; the machines don't. To see why, it helps to actually open the hood: how is a large language model trained? And once you know, the more interesting question almost answers itself — is that anything like how your brain works? Short version: the similarities are real but shallow, and the differences are staggering.
This is a simplified, plain-English explainer of a genuinely complex pipeline. Sources for the figures are at the end.
How an LLM is actually trained
Modern chatbots aren't built in one step. They're raised in three, each turning a more useless thing into a more useful one.
Stage 1 — Pretraining: predict the next word, a trillion times
This is the heavy lifting. The model is shown enormous amounts of text and given one absurdly simple game: predict the next word. Show it "The capital of France is ___" and it learns to guess "Paris." Do that across essentially the whole public internet — books, code, articles, forums — and something remarkable happens: to get good at predicting the next word, the model is forced to absorb grammar, facts, reasoning patterns and style.
Mechanically, "training" here means adjusting billions of internal numbers — called parameters — a tiny bit each time the model guesses wrong, using a process called backpropagation. GPT-3 had 175 billion of these parameters and read on the order of 300 billion words; Meta's Llama 3 was trained on over 15 trillion tokens. This is why training is so expensive: it runs for months across thousands of specialized chips. One estimate put the electricity for training GPT-3 alone at around 1,287 megawatt-hours — roughly what 130 US homes use in a year.
The output of all this is a "base model": a spectacularly good text-completer that is also nearly useless as an assistant. Ask it a question and it might just continue with more questions, because that's what text on the internet often does. It has knowledge but no manners. (For how that knowledge actually gets used once training is done, see how ChatGPT actually works.)
Stage 2 — Supervised fine-tuning: learn to be an assistant
Next, humans step in. They write thousands of example exchanges — a question and an ideal answer — and the base model is trained to imitate them. This is supervised fine-tuning (SFT), and it's what converts a raw text-predictor into something that behaves like a helpful assistant: it learns the format of being useful — answer the question, be clear, stop talking when you're done.
Stage 3 — RLHF: learn what people actually prefer
The final polish is the clever bit. The model generates several answers to a prompt, and humans rank them from best to worst. Those rankings train a separate "reward model" that learns to score answers the way people would — and the LLM is then tuned to maximize that score. This is Reinforcement Learning from Human Feedback (RLHF), the technique (introduced for InstructGPT in 2022) that made ChatGPT feel helpful, polite and reasonably safe rather than merely fluent.
That's the whole arc: read everything → copy good examples → optimize for human preference. Now hold it up against a brain.
So — is that how you learn?
Here's where the metaphor gets exposed. Yes, there are genuine echoes: both an LLM and a brain are networks of simple units, and both "learn" by adjusting the strength of connections between those units in response to data. That shared shape is why the borrowed vocabulary isn't pure marketing.
But look one level deeper and the two could hardly be more different.
- You never stop learning. An LLM does it once, then freezes. Your brain rewires itself every day. A trained LLM is static — it doesn't learn from your conversation (beyond the short-term memory of the current chat). Teaching it something new means an expensive retraining run, not a moment of insight.
- Your brain runs on a light bulb's worth of power. ~86 billion neurons, sipping about 20 watts. Training a single large model can burn through more electricity than a person uses in a lifetime. Biological intelligence is breathtakingly efficient; today's AI is brute force.
- The mechanisms aren't the same. LLMs learn by backpropagation and gradient descent — a global, mathematical error-correction the brain almost certainly does not use. Brains learn locally and continuously, through biological plasticity we still don't fully understand.
- And the biggest one: grounding. You learned language while living in a world — touching, seeing, wanting, failing. An LLM learned language from text alone. It has never seen the rain it writes about. It models the statistical patterns of how words follow words, not the reality the words point to.
The data gap that says it all
If one fact captures the difference, it's this.
Researchers behind the "BabyLM" project estimate that a child is exposed to around 100 million words by about age 13 — and from that modest input, plus a body and a world, the child achieves genuine understanding. A frontier model like Llama 3 trains on more than 15 trillion words — on the order of 150,000 times more language — to achieve fluent prediction.
That's the tell. If LLMs learned like brains, they wouldn't need a hundred-thousand times more data to fall short of a teenager's grasp of meaning. More data is not more understanding. It's the difference between a system that has lived and one that has only read.
Then why does it feel so human?
Because it is built, quite literally, out of us. An LLM is trained on a vast corpus of human writing, so it reflects our knowledge, our turns of phrase, even our biases back at us. It's less a digital brain than a mirror of human text — astonishingly capable, but capable in a different way than we are.
This is the live debate in AI: skeptics call LLMs "stochastic parrots" that merely remix patterns; others point out that at sufficient scale, genuinely useful new abilities emerge from that pattern-matching. Both can be true. The model can be "just" predicting the next word and be extraordinarily useful — the way evolution is "just" survival and still produced the eye.
Why this matters
Understanding how LLMs are trained isn't trivia — it changes how you should use them:
- It explains hallucinations. A model that was trained to produce plausible text, not to retrieve verified truth, will sometimes generate confident nonsense. That's not a glitch; it's the training objective showing through.
- It tempers the hype — both ways. Knowing it's not a brain guards against over-trusting it as an all-knowing oracle. Knowing what scale produces guards against dismissing it as a mere toy.
- It tells you where to be careful. Because it's frozen after training, its knowledge has a cutoff and it can't truly learn your corrections mid-conversation. Because it's ungrounded, it needs you to supply the judgment about what's real.
Frequently Asked Questions
How are LLMs trained, in simple terms?
In three stages: pretraining (the model reads enormous amounts of text and learns to predict the next word, tuning billions of parameters), supervised fine-tuning (humans provide example question-and-answer pairs so it learns to act like an assistant), and RLHF (humans rank answers so the model learns to produce ones people prefer).
Do LLMs have "neurons" like a brain?
Only by loose analogy. An LLM's "neurons" are numbers in a mathematical network — billions of parameters adjusted during training. They're inspired by biological neurons but work very differently, and there's nothing biological about them.
Do LLMs keep learning after they're trained?
No. Once training finishes, the model is frozen. It doesn't learn from your chats (beyond remembering within the current conversation). Adding new knowledge requires fine-tuning or a fresh training run — it can't have a "moment of insight" the way you can.
How much data is an LLM trained on?
A lot. GPT-3 read roughly 300 billion words; newer models like Llama 3 train on 15+ trillion tokens. For comparison, a human child encounters only about 100 million words by age 13 — so LLMs see on the order of 100,000 times more language.
Is an LLM actually intelligent — does it understand?
It depends what you mean by "understand." LLMs model the statistical patterns of language extremely well and can reason impressively, but they aren't grounded in the real world the way humans are — they've never experienced what they describe. Most researchers see them as powerful pattern-learners, not minds.
The bottom line
An LLM is trained by reading a near-bottomless ocean of human text, learning to predict what comes next, then being shaped by human examples and human preferences into something helpful. Your brain learns from a trickle of words, a body, and a lifetime in the world — continuously, on twenty watts, grounded in reality.
They share a metaphor and almost nothing else. That's not a knock on AI — what these systems do is genuinely remarkable. But the next time a chatbot dazzles you, remember what's behind the curtain: not a mind that thinks like yours, but a magnificent mirror, trained on everything we've ever written.
Related on PrimusSource: How ChatGPT Actually Works, Getting Started with AI Agents and Claude vs. ChatGPT in 2026: Which AI Is Actually Better?.
Sources
- Language Models are Few-Shot Learners (GPT-3) — Brown et al., 2020
- Training language models to follow instructions with human feedback (InstructGPT / RLHF) — Ouyang et al., 2022
- Introducing Meta Llama 3 (15+ trillion training tokens) — Meta AI
- The BabyLM Challenge: sample-efficient pretraining on a developmentally plausible corpus — Warstadt et al., 2023
- Carbon Emissions and Large Neural Network Training (GPT-3 energy) — Patterson et al., 2021
- The human brain in numbers (~86 billion neurons) — Herculano-Houzel, 2009
- The human brain runs on less power than a light bulb (~20 watts) — Encyclopædia Britannica
