Does an LLM understand things the way people do?

That is debated. What is clear is that it learns statistical patterns from text well enough to reason, explain, and code convincingly, but it can also produce confident errors, so verify important outputs.

Why do LLMs sometimes give different answers to the same question?

Generation involves choosing among likely next tokens, so outputs can vary between runs. Some APIs let you control how much randomness is used; others fix those settings.

How Large Language Models Work: A Practical Introduction

Introduction

Large language models (LLMs) power chat assistants, coding tools, and agents. You do not need to build one to use one well, but understanding the basics explains their strengths and their odd failures. This first lesson of the LLM fundamentals course covers how they work at a practical level.

Prerequisites

No machine learning background required
Curiosity about how AI assistants produce answers

Step-by-Step Guide

Step 1: Think of an LLM as a next-token predictor

At its core, an LLM reads a sequence of text and predicts what comes next, one small piece at a time. Chaining those predictions produces whole sentences, code, or analyses. Because it predicts likely continuations, it can be fluent and still wrong.

Step 2: Learn what a token is

Models do not read characters or words. They read tokens, which are chunks such as whole short words, parts of longer words, punctuation, or spaces. Everything the model sees and produces is measured in tokens, which also determines cost and limits.

Step 3: Meet the transformer and attention

Modern LLMs are built on the transformer architecture. Its key idea, attention, lets the model weigh how relevant every earlier token is to the one it is generating. That is how it keeps track of context such as which noun a pronoun refers to, across long passages.

Step 4: Follow the training stages

Pretraining teaches the model language and a great deal of world knowledge by predicting text across huge datasets. Fine-tuning on curated examples teaches it to follow instructions and adopt useful behavior. Further alignment training, often using human or AI feedback on which answers are better, shapes style, helpfulness, and safety.

Step 5: Know what that means in practice

LLMs are strong at language tasks: drafting, summarizing, translating, explaining, and writing code. They are weaker at exact arithmetic, up-to-the-minute facts, and anything needing guaranteed accuracy unless you give them tools or reliable source material. The rest of this course shows how to work with those strengths and limits.

FAQs

Does an LLM understand things the way people do?: That is debated. What is clear is that it learns statistical patterns from text well enough to reason, explain, and code convincingly, but it can also produce confident errors, so verify important outputs.
Why do LLMs sometimes give different answers to the same question?: Generation involves choosing among likely next tokens, so outputs can vary between runs. Some APIs let you control how much randomness is used; others fix those settings.

Summary

LLMs predict the next token using the transformer architecture, trained in stages from raw text to helpful assistants. That explains both their fluency and their tendency to be confidently wrong.