How Large Language Models Actually Work: A Plain-English Guide

If you’ve used ChatGPT, Claude, or any other AI chatbot recently, you’ve experienced something genuinely remarkable: a machine that can write essays, code, poetry, and responses that feel almost human. Yet most people have no idea what’s happening under the hood. You might have heard terms like “neural networks,” “transformers,” or “tokens” thrown around, but they remain fuzzy abstractions. This article demystifies how large language models actually work—without requiring a PhD in computer science.

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Here’s the thing most people miss about this topic.

As someone who works with knowledge workers daily, I’ve noticed that understanding the fundamentals of AI systems makes you a smarter consumer of these tools and a better decision-maker in a rapidly changing professional landscape. Whether you’re considering adopting AI at your organization, evaluating its limitations, or simply wanting to stay informed, knowing how large language models operate is increasingly essential.

What Exactly Is a Large Language Model?

A large language model (LLM) is essentially a sophisticated pattern-matching system trained on vast amounts of text data. It’s not “thinking” in the way humans think—it’s predicting the next word based on the probability of what typically follows given sequences. When you type a prompt into ChatGPT, the model isn’t accessing a database of pre-written responses or consulting the internet. Instead, it’s performing billions of mathematical calculations extremely quickly to generate the most statistically likely next word, then the next, then the next.

The “large” part is key. We’re talking about models with billions or even trillions of parameters—mathematical weights that have been tuned through training on enormous datasets. GPT-4, for example, likely has parameters numbering in the hundreds of billions. This scale matters because it directly contributes to the model’s ability to capture nuanced patterns in human language (Vaswani et al., 2017).

Think of it this way: if you’ve read enough mystery novels, you develop an intuition about where the plot might go, what clues matter, and how the ending typically unfolds. An LLM has read vastly more text than any human ever could, and it’s internalized patterns so deeply that it can generate coherent, contextually appropriate responses. But it’s doing this through mathematics, not understanding.

The Building Block: Tokens and Embeddings

Before a language model can process your text, it breaks it into smaller units called tokens. These aren’t always individual words—a token might be a word, a part of a word, or even punctuation. The model needs this subdivision because it works more efficiently with manageable pieces. When you ask ChatGPT a question, your prompt gets tokenized immediately.

Once tokenized, each token gets converted into a numerical representation called an embedding. Imagine embedding as a location in an abstract multidimensional space. Words with similar meanings end up close to each other in this space. The word “king” and “queen” have embeddings that are positioned similarly, while “king” and “potato” are far apart. This spatial representation is crucial because it allows the model to reason about meaning mathematically (Bengio et al., 2013).

This happens through a simple but elegant mathematical trick: each token is assigned a vector (a list of numbers), and these vectors are learned during training. The model learns that certain numerical patterns correspond to concepts, relationships, and meanings. You don’t need to understand the math to grasp the principle: language gets converted to numbers that capture meaning.

The Transformer Architecture: The Secret Sauce

The breakthrough that made modern large language models possible was the transformer architecture, introduced in a 2017 paper titled “Attention Is All You Need.” If you want to understand how large language models actually work, understanding transformers is the single most important thing.

Here’s the core insight: when processing a sentence, different words matter differently depending on context. In “The cat sat on the mat,” the word “cat” is central to understanding what the sentence is about. But in “I gave the cat a treat because the cat was hungry,” the first “cat” has less relevance to the second part of the sentence.

Transformers solve this through a mechanism called self-attention. As the model processes each word, it can ask: “Which other words in this sequence are most relevant to understanding me?” It computes attention scores—essentially weights—for how much each word should influence the interpretation of every other word. This happens in parallel for all words and all positions, making transformers incredibly efficient.

The architecture consists of stacked layers of these attention mechanisms, combined with feed-forward neural networks. As information flows through these layers, the model gradually refines its understanding of the input, building up increasingly abstract representations. The lowest layers might capture basic grammatical patterns, while deeper layers understand semantic meaning and long-range relationships (Devlin et al., 2018).

What makes this so powerful is that attention lets the model weigh relevance dynamically. Unlike earlier approaches that struggled with long-range dependencies, transformers can maintain awareness of information from far back in a sequence. This is why modern LLMs can write coherent multi-page essays without losing the thread of an argument.

Training: How Models Learn Patterns From Text

Large language models are trained through a process called unsupervised learning on enormous text corpora—sometimes hundreds of billions of words scraped from books, websites, academic papers, and other sources. The training process is surprisingly simple in concept: the model is shown a sequence of tokens, and it’s tasked with predicting the next token. It makes a guess, calculates how wrong it was, and adjusts its internal parameters to improve.

This happens billions or trillions of times. Each adjustment is tiny—fractional changes to numerical weights—but across the entire dataset, these micro-adjustments compound. The model gradually internalizes statistical patterns: which word sequences are common, which are rare, which are grammatical, which violate expectations.

The computational cost is staggering. Training a state-of-the-art large language model requires weeks of processing on thousands of specialized hardware accelerators (GPUs) and costs tens of millions of dollars. Once trained, however, using the model is relatively cheap. You run the mathematical operations once, get your answer, and you’re done.

After initial training, models are typically refined through a process called fine-tuning, where human feedback is incorporated. For instance, ChatGPT was fine-tuned using reinforcement learning from human feedback (RLHF). Humans rated different model outputs, and the model learned to prioritize generating responses that humans found helpful, harmless, and honest. This is why ChatGPT refuses certain requests or apologizes when it makes mistakes—it’s been shaped to do so through training signals.

Have you ever wondered why this matters so much?

What Large Language Models Can Actually Do (And Can’t)

Understanding how large language models actually work clarifies both their capabilities and their limitations. These models excel at:

Last updated: 2026-04-15

Your Next Steps

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About the Author

Written by the Rational Growth editorial team. Our health and psychology content is informed by peer-reviewed research, clinical guidelines, and real-world experience. We follow strict editorial standards and cite primary sources throughout.

References

Li, Y., et al. (2025). Large language models in clinical trials: applications, technical challenges, and future directions. Frontiers in Artificial Intelligence. Link
Alghamdi, J., et al. (2025). Evaluating large language models: a systematic review of efficiency, transparency, and applications. Frontiers in Computer Science. Link
Zhao, W. X., et al. (2025). LLMs for Explainable AI: A Comprehensive Survey. arXiv preprint arXiv:2504.00125. Link
ACM Digital Library. (2025). A Comprehensive Overview of Large Language Models. ACM Computing Surveys. Link
Raschka, S. (2025). LLM Research Papers: The 2025 List (January to June). Ahead of AI Magazine. Link
MIT News. (2025). A new way to increase the capabilities of large language models. MIT News. Link

What is the key takeaway about how large language models actually work?

Evidence-based approaches consistently outperform conventional wisdom. Start with the data, not assumptions, and give any strategy at least 30 days before judging results.

How should beginners approach how large language models actually work?

Pick one actionable insight from this guide and implement it today. Small, consistent actions compound faster than ambitious plans that never start.

How Large Language Models Actually Work: A Plain-English Guide