Harman Panwar

Posted on Jul 1

Understanding Large Language Models: A Beginner's Guide

#webdev #ai #programming #software

If you've ever typed a question into ChatGPT and gotten back a surprisingly thoughtful answer in seconds, you've probably wondered: how does it actually do that? This article breaks down what a Large Language Model (LLM) is, what happens behind the scenes when you send it a message, and the key ideas — tokens and Transformers — that make it all possible.

No prior technical background needed. Let's start from the beginning.

1. What is an LLM?

LLM stands for Large Language Model. It's a type of artificial intelligence system trained to understand and generate human language. "Large" refers to two things at once: the enormous amount of text it learns from during training, and the huge number of internal parameters (think of them as adjustable knobs) the model uses to make its predictions — often numbering in the billions.

What problem do LLMs actually solve?

Before LLMs, computers were good at rigid, rule-based tasks but bad at anything involving the messiness of real human language — sarcasm, ambiguity, context, tone, and nuance. If you wanted a computer to summarize an article, answer an open-ended question, or write a coherent paragraph, older systems either failed outright or produced clunky, robotic results.

LLMs solve this by learning the patterns of language itself — how words and ideas relate to each other — from massive amounts of text. This lets them do things that used to require a human: answering questions, writing essays, translating languages, summarizing documents, debugging code, and holding natural conversations.

Popular examples of LLMs

Some of the most well-known LLMs today include:

GPT models (which power ChatGPT), developed by OpenAI
Claude, developed by Anthropic
Gemini, developed by Google
Llama, developed by Meta

Each of these is built on similar underlying principles (which you'll learn about in this article), even though the companies train and fine-tune them differently.

Common applications in daily life

LLMs have quietly become part of everyday routines:

Writing assistance — drafting emails, essays, or social media posts
Customer support chatbots — answering questions on company websites
Search and research — summarizing information or answering factual questions
Coding help — writing, explaining, and debugging code
Translation — converting text between languages
Voice assistants — powering more natural conversations with devices

If you've used autocomplete on your phone's keyboard, you've actually interacted with a distant, much simpler relative of an LLM — one that predicts your next word based on what you've typed so far.

2. What Happens When You Send a Message to ChatGPT?

It feels instantaneous, but a surprising amount happens in the few seconds between hitting "send" and seeing a response appear.

Step 1: Typing a prompt

You type your message — your prompt — into the chat box. This could be a question, an instruction, or the continuation of an ongoing conversation.

Step 2: Processing your message

Once you hit send, your message (along with relevant conversation history) is sent to the model. But here's the catch: the model doesn't read words the way you do. It first has to convert your text into a numerical format it can actually work with. This involves breaking your message into tokens and turning those tokens into numbers — a process we'll unpack in detail in Sections 3 and 4.

Step 3: Generating a response

Once your message is in a format the model can process, it generates a response one small piece at a time. At each step, the model calculates which token is most likely to come next, based on everything that came before it — your prompt, the conversation so far, and patterns it learned during training. It picks a token, adds it to the response, then repeats the process to generate the next one, and the next, until the response is complete. This is why responses often appear to "stream in" gradually rather than showing up all at once — because that's genuinely how they're built, piece by piece.

Why responses are not copied from the internet

This is one of the most common misconceptions about LLMs: people assume the model is searching the internet and pasting back something it found. It isn't (unless it's specifically using a web search tool as part of the product). Instead, during training, the model studied huge amounts of text and learned statistical patterns about how language works — which words tend to follow other words, how ideas are typically structured, what a good answer to a question tends to look like.

When it responds to you, it's generating new text based on those learned patterns, not retrieving a pre-written answer from a database. This is also why an LLM can write something that has never existed before — a poem about your specific pet, an explanation tailored to your exact question — and why it can occasionally get things wrong or "make things up," since it's predicting plausible-sounding text rather than looking up verified facts.

3. Why Computers Don't Understand Human Language

To really get how LLMs work, it helps to step back and ask a more basic question: why is this even hard for a computer in the first place?

Text vs. numbers

At their core, computers are number-crunching machines. Every operation a computer performs — whether it's rendering a video, running a spreadsheet formula, or displaying this article — ultimately comes down to mathematical operations on numbers. Human language, on the other hand, is symbolic and messy. Words can have multiple meanings depending on context ("bank" of a river vs. a "bank" that holds money), sentences can be structured in countless ways, and meaning often depends on things a computer doesn't natively grasp, like tone or cultural context.

Why computers need everything converted into numbers

Because computers only really "think" in numbers, any text you feed into a model has to be translated into a numerical form before any processing can happen. This isn't unique to language, either — images are converted into grids of numbers representing pixel colors, and audio is converted into numbers representing sound wave amplitudes over time. Language is no different in principle; it just needs its own translation method.

Introduction to tokens

This is where tokens come in. Rather than trying to convert entire sentences or paragraphs into numbers all at once, models break text down into smaller, manageable chunks called tokens. Each token can then be mapped to a number, and eventually to a richer numerical representation the model can actually compute with. Tokens are the bridge between human-readable text and the math that powers an LLM — which brings us to the next section.

4. Tokenization

What tokens are

A token is a chunk of text — it might be a whole word, part of a word, a single character, or a punctuation mark. Tokenization is simply the process of breaking a piece of text into these chunks so a model can work with them.

Common, frequently-used words are often represented by a single token. Less common or longer words might get split into two or more tokens. Punctuation marks typically count as their own tokens too.

Why tokenization is needed

Tokenization solves two problems at once:

It gives the model a manageable, finite "vocabulary" to work with, instead of having to handle infinite possible words and phrases individually.
It lets the model process even unfamiliar or made-up words, by breaking them down into smaller, more common pieces it already recognizes — rather than getting stuck on a word it's never seen before.

Words vs. tokens

It's tempting to assume "one word equals one token," but that's not quite right. Some words map neatly to one token. Others, especially longer, rarer, or technical words, get split into multiple tokens. This is why the "word count" of a message and its "token count" are usually different numbers — and it's also why AI tools often measure limits and costs in tokens rather than words.

Simple examples

Here's a concrete illustration of how counterintuitive tokenization can be: a string of digits like "1234567890" and a single word like "underlying" have roughly the same number of characters — but the digit string breaks into several tokens, while the word "underlying" might be represented as just one. Similarly, capitalization and spacing can change how a word is tokenized: "red" at the start of a sentence, "red" in the middle of a sentence, and "Red" with a capital letter can each be encoded as different tokens, even though a human reader would consider them essentially the same word.

If you want to see this in action yourself, OpenAI provides a free interactive tokenizer tool where you can paste in any text and watch it get broken into color-coded tokens in real time — a genuinely useful way to build intuition before writing your own examples.

5. Transformers

What a Transformer is

The Transformer is a neural network architecture — a specific way of designing an AI model — that was introduced in 2017 specifically to handle language tasks better than anything before it. Nearly every major LLM in use today, including GPT, Claude, Gemini, and Llama, is built on some version of this architecture.

Why it changed AI

Before Transformers, language models typically processed text sequentially — reading one word, then the next, then the next, carrying forward a kind of "memory" of what came before. This approach struggled with long sentences or documents, because important context from early on would often get diluted or lost by the time the model reached the end.

Transformers introduced a mechanism called self-attention, which allows the model to look at all the words in a piece of text at once, and weigh how relevant each word is to every other word — regardless of how far apart they are. This was a major shift: instead of processing text step-by-step, Transformers could process it in parallel, which made both understanding and training dramatically faster and more effective.

How it helps understand language

Self-attention is easiest to grasp with an example. Consider the sentence: "The trophy didn't fit in the suitcase because it was too big." What does "it" refer to — the trophy, or the suitcase? A human reader understands from context that "it" means the trophy. Self-attention gives a Transformer a similar ability: as it processes the word "it," it can look back at every other word in the sentence and figure out which ones are most relevant to understanding it — in this case, correctly connecting "it" back to "trophy." This is what allows Transformer-based models to keep track of context across long passages of text, not just the last few words.

Why almost every modern LLM uses Transformers

Two things made Transformers the foundation of modern AI:

Parallelization — because Transformers don't need to process text strictly in order, they can be trained far more efficiently on modern hardware, which made it practical to train models on truly massive amounts of text.
Better context handling — self-attention captures relationships between words more effectively than earlier architectures, leading to more coherent, contextually accurate outputs.

Together, these advantages are why virtually every state-of-the-art language model today, from chatbots to coding assistants, is built on the Transformer architecture.

Bringing It All Together

Here's the full picture, start to finish: you type a message, which gets broken into tokens because computers can only work with numbers, not raw text. Those tokens are processed by a Transformer-based model, which uses self-attention to understand how every part of your message relates to every other part — and to the broader conversation. The model then generates a response one token at a time, drawing on patterns it learned from enormous amounts of text during training, rather than copying or retrieving anything from the internet.

It's a remarkable pipeline — and now, hopefully, a much less mysterious one.

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