🧠 What Is an LLM? How Large Language Models Work — Explained Simply

The One-Sentence Explanation

A Large Language Model (LLM) is a type of artificial intelligence that was trained on enormous amounts of text, and learned to predict what word (or token) comes next in a sequence — so well that it can write coherent paragraphs, answer questions, write code, summarize documents, and hold conversations.

What Makes a Language Model "Large"?

Before the current generation of LLMs, language models existed but were small: trained on limited data, capable of only narrow tasks. What changed starting around 2017–2020 was scale — researchers discovered that training models with billions (then hundreds of billions) of parameters on trillions of words of text produced systems with dramatically more capable and general abilities.

The "large" refers to the number of parameters — adjustable numerical weights inside the neural network. GPT-2 had 1.5 billion parameters. GPT-4 is estimated to have hundreds of billions. More parameters + more training data + more compute = better capabilities, though with diminishing returns.

How LLMs Are Trained

Training an LLM happens in stages. The first stage — pretraining — feeds the model enormous datasets: websites, books, code repositories, scientific papers, and more. The model tries to predict the next token (roughly, the next word or word fragment). When it predicts wrong, the error signal adjusts the parameters slightly. This happens billions of times, gradually shaping the model's weights to encode patterns in language, facts, reasoning, and style.

Pretraining produces a model that can generate text — but not necessarily helpful or safe text. The second stage — fine-tuning with RLHF (Reinforcement Learning from Human Feedback) — trains the model to be more helpful, harmless, and honest by having human raters score its outputs and using those ratings to further adjust the model. This is what makes ChatGPT answer questions rather than just predict the next word in a document.

What Are Tokens?

LLMs don't process letters or words directly — they work with tokens, which are chunks of text roughly equivalent to syllables or common words. "engineering" might be one token; "uncharacteristically" might be split into three. GPT-4 can process about 128,000 tokens in one context window (roughly 100,000 words). The context window is how much the model can "see" at once — it has no memory of previous conversations beyond what's in the current window.

Why LLMs Seem to "Know Things"

During training on trillions of words, the model's parameters encoded statistical patterns that correspond to real-world knowledge. When you ask what the capital of France is, the model outputs "Paris" not because it looked it up, but because "Paris" follows "capital of France" in its training data overwhelmingly often. It is a very sophisticated pattern matcher — but one whose patterns are so rich and interconnected that they often constitute genuine understanding.

Why LLMs Make Mistakes (Hallucinations)

LLMs sometimes generate confident-sounding false statements — a phenomenon called hallucination. This happens because the model generates the most statistically likely next token, not necessarily the factually correct one. It doesn't have access to a fact-checking database — it only has its training data and the patterns it learned. For facts that appear rarely or inconsistently in training data, the model can generate plausible-sounding but wrong answers.

This is why LLMs work best when you can verify their outputs (code that either runs or doesn't, documents you'll review before sending, analysis you'll sanity-check). They work worst when used as a sole source of truth for critical decisions without verification.

LLMs in Engineering and Technical Work

For engineers, LLMs are most useful for writing assistance (technical reports, specifications, emails), code generation and debugging, explaining unfamiliar technical topics, and processing or summarizing large documents. Apps like AI Agent Builder and Build Your LLM let you create custom AI tools that combine LLMs with your own data and workflows — without needing a background in machine learning.