How Large Language Models Work: A Parent's Guide

Your kid typed a question into ChatGPT last week and got an answer that sounded completely authoritative. Maybe it was right. Maybe it invented a source. Maybe it was a confident mixture of both. If you’ve ever wondered what exactly is happening inside that black box — and whether your kid should understand it too — this is the article.

I spent fifteen years building consumer electronics at Apple and Samsung, including work on the signal processing chips that power voice assistants. The moment large language models went mainstream, I knew most parents would be handed explanations full of mysticism and vague wonder. What they actually needed was the engineering version: how do these things work, where do they fail, and what does that mean for a kid using them every day?

Key Takeaways

• LLMs don’t retrieve facts — they predict which words are most likely to follow previous words, based on patterns in training data
• “Hallucination” isn’t a bug or a lie — it’s the predictable consequence of how these models are built
• Model size (7B vs. 70B parameters) roughly correlates with capability but never guarantees accuracy
• RLHF (Reinforcement Learning from Human Feedback) makes models more helpful and less offensive, but doesn’t make them truthful
• Kids who understand how LLMs work become more effective users and more appropriate skeptics

What a Large Language Model Actually Is

Start with autocomplete on your phone. When you type “I’m headed to the” — your keyboard might suggest “store,” “gym,” or “office.” It’s guessing what you’ll type next based on patterns it learned from text.

Now scale that up by a factor that strains comprehension. A large language model trains on hundreds of billions of words — books, websites, academic papers, forum posts, Wikipedia, code, legal documents. It doesn’t memorize those words. Instead, it adjusts billions of internal numerical values (called parameters or weights) until the numbers become good at predicting: given these words, what word comes next?

That’s the entire trick. An impressive, consequential, sometimes dangerous trick — but at its core, it’s structured prediction.

What “Tokens” Are and Why They Matter

LLMs don’t process letters or words directly. They process tokens, which are chunks of text that roughly correspond to common word fragments. The word “playing” might be one token. “Unbelievable” might be split into “un,” “believ,” and “able.” A typical page of English text is roughly 250–400 tokens.

This matters for two reasons. First, LLMs have a context window — a maximum number of tokens they can process at once. Early models maxed out at 4,000 tokens (about 3,000 words). Current models handle 128,000 or more, which is roughly a short novel. Second, the model processes the entire context window together, which is part of what makes it able to follow long, complex conversations.

What the Transformer Architecture Actually Does

Modern LLMs are built on an architecture called the transformer, introduced in a 2017 Google paper titled “Attention Is All You Need” (Vaswani et al., 2017). Before transformers, language models had to process text sequentially — word by word, in order. Transformers introduced self-attention, which lets the model look at every word in the input simultaneously and figure out which words are most relevant to each other.

Think of it this way: if you’re reading the sentence “The cat sat on the mat because it was warm,” the word “it” refers to “the mat.” A transformer can learn to link “it” to “mat” regardless of how far apart they are in the sentence. Self-attention is how the model tracks what relates to what across long stretches of text.

Multiple attention “heads” run in parallel, each looking for different types of relationships. Then the results get combined. This happens in dozens or hundreds of “layers” stacked on top of each other. The deepest layers tend to handle abstract conceptual relationships; the earliest layers handle surface-level syntax.

How Training Actually Works

Training an LLM is not programming in the traditional sense. You don’t write rules. You expose the model to text, ask it to predict the next token, measure how wrong it was, and adjust the parameters slightly in a direction that would have made a better prediction. Repeat this billions of times. This process is called gradient descent.

The compute required is staggering. Training GPT-4 is estimated to have required tens of millions of dollars in cloud computing (Patel & Wong, 2023). Training runs on thousands of specialized chips (GPUs or TPUs) running for weeks.

What RLHF Does (and Doesn’t Do)

After initial training, most modern LLMs go through Reinforcement Learning from Human Feedback (RLHF). Human raters compare different model outputs and mark which ones are more helpful, accurate, or appropriate. The model learns to produce outputs that humans rate highly.

RLHF makes models significantly more useful in conversation. It reduces offensive outputs. It teaches the model to follow instructions more reliably. But here’s what it does not do: it doesn’t teach the model to know what’s true. It teaches the model to produce outputs that seem accurate and helpful to a human rater. These are related but not the same thing.

Why LLMs Hallucinate (And Why That Won’t Be Fully Solved)

“Hallucination” is the term for when an LLM confidently states something false. It might invent a paper that doesn’t exist, a statistic with the wrong number, or a court case with the right general shape but the wrong outcome.

The mechanism isn’t mysterious once you understand the architecture: the model is always doing the same thing — predicting probable text. If the training data contains patterns that a scientist named Dr. Chen published research in a particular subfield, and the model is asked to name experts in that field, it will generate a name that fits the pattern even if that specific person never published that specific paper.

A 2023 study from Stanford’s Human-Centered AI Institute found hallucination rates in medical queries from popular LLMs ranging from 5% to 37% depending on question type (Singhal et al., 2023). In legal and financial domains, researchers at MIT found similar rates of confident factual errors (Bommarito & Katz, 2022).

The reason this is hard to solve: the model has no internal “truth sensor.” It has patterns. A model that says “I’m not sure” on every uncertain question would be less useful. So models are trained to be helpful and confident, which creates systematic overconfidence.

Model Size	Approximate Parameters	Relative Capability	Known Hallucination Pattern
Small (7B)	7 billion	Good for simple tasks, writing	Hallucinates more freely on niche facts
Medium (13–34B)	13–34 billion	Strong reasoning, code, analysis	Improved but still unreliable on citations
Large (70B+)	70 billion+	Near GPT-4 tier in many tasks	Better calibration, still hallucinates
Frontier (GPT-4, Claude 3+)	Estimated 100B–1T+	Broad capability, longer context	Lowest hallucination rates but not zero

What Model Size Actually Means

When you see “7B” or “70B,” the number refers to the parameter count — how many adjustable numerical values the model has. More parameters generally means the model can learn more complex patterns and relationships in language.

But bigger isn’t always better in practice. A 7-billion-parameter model that has been carefully fine-tuned on a specific domain can outperform a 70-billion-parameter general model on tasks in that domain. And a smaller model runs faster and cheaper, which matters for applications that need real-time responses.

The real frontier isn’t just size. It’s training data quality, architecture improvements, and fine-tuning techniques. The jump from GPT-3 to GPT-4 wasn’t just more parameters — it involved fundamental changes in how the model was trained and evaluated.

How to Teach Your Kid About Large Language Models

Ages 5–8: The “Finish the Sentence” Game

Sit together and take turns finishing each other’s sentences. “The dog ran toward the…” — let your child guess what comes next. Then explain that this is exactly what ChatGPT does, except it’s read millions of sentences and gets really good at guessing. Ask them: does guessing the next word mean you know things are true? Help them understand that smart-sounding guesses can still be wrong.

Ages 9–12: The Pattern Detective

Ask your child to write a few sentences in their own style about something they care about (video games, soccer, a book). Then ask ChatGPT to write in a similar style on the same topic. Compare. Where does the AI get it right? Where does it feel “off”? This exercise makes the pattern-matching nature of LLMs tangible — the AI has patterns, but it doesn’t have your voice or your actual knowledge.

Ages 13+: Fact-Check a ChatGPT Response

Pick a topic your teen knows something about. Have them ask ChatGPT a detailed question and then fact-check every specific claim against primary sources (Wikipedia at minimum, ideally journal abstracts or news articles). Keep a tally: correct, partially correct, wrong, invented. This becomes a practical media literacy skill that transfers to every domain. Discuss: what should you use LLMs for, given what you found?

The question to ask: “If ChatGPT is just predicting the next word — how would it know if a ‘fact’ it generates is actually true?”

What to Watch For Over the Next 3 Months

LLMs are evolving rapidly. Over the next 90 days, watch for:

Month 1: Notice when your kid uses ChatGPT or similar tools. Are they copying answers directly or treating them as a starting point? The habit of verification needs to be established early.

Month 2: Try a deliberate hallucination hunt together. Ask an LLM about something obscure — a local event, a specific minor-league athlete, a small company. The error rate goes up sharply on topics with thin coverage in training data.

Month 3: Watch for capability improvements. OpenAI, Anthropic, and Google release model updates frequently. New versions often reduce hallucination rates on common tasks while introducing new behaviors. Teaching kids to re-evaluate tools as they change is the meta-skill here.

Frequently Asked Questions

Is ChatGPT the same as an LLM?

ChatGPT is a product built on top of an LLM (GPT-4 or its successors). The LLM is the underlying model. ChatGPT adds a conversation interface, safety filtering, and product features around it. Other LLMs include Claude (Anthropic), Gemini (Google), Llama (Meta), and dozens of open-source models.

Why does ChatGPT sometimes give different answers to the same question?

LLMs use a parameter called “temperature” that controls randomness in token selection. At temperature 0, the model always picks the most probable next token (deterministic). At higher temperatures, it sometimes picks less probable tokens, creating variety. Most consumer products use temperatures above 0, which means identical questions can produce different answers.

Are LLMs actually intelligent?

This depends entirely on what you mean by “intelligent.” LLMs can solve many problems that would require significant intelligence in a human — complex reasoning, code debugging, creative writing. But they have no goals, no awareness, no persistent memory between conversations (unless explicitly provided), and no understanding in the philosophical sense. They’re extraordinarily capable pattern-matching systems.

Should my kid be allowed to use LLMs for homework?

That depends on your child’s age, the assignment, and what you’re trying the assignment to accomplish. Using an LLM to generate an essay defeats the purpose of learning to write. Using one to explain a concept and then writing independently is more like using a good textbook. The key distinction is: did the kid think, or did the kid just copy? Most educators are developing explicit policies — check with your school.

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About the author Ricky Flores is the founder of HiWave Makers and an electrical engineer with 15+ years of experience building consumer technology at Apple, Samsung, and Texas Instruments. He writes about how kids learn to build, think, and create in a tech-saturated world. Read more at hiwavemakers.com.

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Sources

1. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). “Attention Is All You Need.” Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
2. Singhal, K., Azizi, S., Tu, T., et al. (2023). “Large Language Models Encode Clinical Knowledge.” Nature, 620, pp. 172–180. https://doi.org/10.1038/s41586-023-06291-2
3. Bommarito, M., & Katz, D. M. (2022). “GPT Takes the Bar Exam.” SSRN Preprint. https://doi.org/10.2139/ssrn.4314839
4. Patel, D., & Wong, A. (2023). “The Inference Cost of GPT-4.” SemiAnalysis. https://www.semianalysis.com/p/gpt-4-architecture-infrastructure
5. Ouyang, L., Wu, J., Jiang, X., et al. (2022). “Training Language Models to Follow Instructions with Human Feedback.” arXiv. https://arxiv.org/abs/2203.02155
6. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” FAccT 2021. https://doi.org/10.1145/3442188.3445922