What Is an LLM? How ChatGPT, GPT & AI Language Models Really Work (Beginner Guide)

Source: Dev.to

Learn how Large Language Models (LLMs) like ChatGPT work. Understand tokens, GPT, transformers, and how AI generates human‑like text in simple terms.

If you’ve used ChatGPT, Gemini, or Claude, you’ve already interacted with a Large Language Model (LLM). It feels like chatting with a human, but behind the scenes it’s all math, data, tokens, and probabilities.

In this article you will learn:

• What an LLM is
• How LLMs are trained
• What tokens are and how they work
• The meaning of GPT
• How LLMs generate answers step by step

1. What Is an LLM?

LLM = Large Language Model

An LLM is an AI system trained to:

• Understand human language
• Generate human‑like responses

Example

“Explain recursion like I’m 10.”

LLMs let humans talk to computers using natural language instead of code, making AI accessible without programming knowledge.

2. How Are LLMs Trained?

LLMs are trained on massive datasets that include:

• Books
• Blogs
• Articles
• Code repositories
• Web content

Unlike a database, an LLM doesn’t store facts verbatim. It learns patterns, relationships, and probabilities in language—much like how humans improve by reading more.

3. Tokens: How AI Understands Text

Computers don’t understand words—they understand numbers.

When you type:

Hello world

it might be converted to something like:

[15496, 995]

This process is called tokenization and is how LLMs turn text into a format they can process.

Workflow of AI text generation

Text → Tokens → Model → Tokens → Text

• Tokenization – converts text into numbers (tokens).
• Model processing – predicts the next token based on input and learned patterns.
• Detokenization – converts output tokens back into human‑readable text.

4. Input Tokens vs. Output Tokens

• Input Tokens – the message or question you send to the AI.
• Output Tokens – the AI’s generated response.

The model predicts one token at a time, continuing until a complete response is formed—similar to an advanced autocomplete system.

5. What Does GPT Mean?

GPT = Generative Pretrained Transformer

5.1 Generative

LLMs generate responses on the fly rather than retrieving them from a database.

You: “Call me Captain Dev”
LLM: “Sure, Captain Dev!”

The reply is original, created from patterns the model learned during training.

5.2 Pretrained

Before any user interaction, LLMs undergo extensive training on large datasets. Like humans, they learn first, then generate.

5.3 Transformer

The transformer is the neural‑network architecture that powers modern LLMs. It enables the model to process context effectively and predict the next token accurately.

All major LLMs use transformer‑based architectures, e.g.:

• GPT (OpenAI)
• Gemini (Google)
• Claude (Anthropic)
• Mistral

In short, they are Generative + Pretrained + Transformers.

6. How LLMs Generate Answers Step by Step

Think of an LLM as a super‑smart autocomplete system:

1. You type: “The sky is…”
2. The model predicts the next token: “blue”
3. It predicts the following token: “today”
4. It continues token‑by‑token until the full response is complete.

This incremental generation allows LLMs to produce long, coherent responses based on the given context.

7. Real‑World Example

Prompt: “Write a short introduction about yourself for a portfolio website.”

Process

1. Input: The AI receives your text (input tokens).
2. Prediction: The model predicts the next word/token using its pretraining and the provided context.
3. Iteration: It repeats token‑by‑token until the response is finished.
4. Output: Detokenization turns the tokens into readable text you can copy and use.

That’s why AI can generate blog posts, code snippets, summaries, and more instantly.

8. Final Thoughts

LLMs are reshaping how humans interact with machines. Instead of humans learning programming languages, machines are learning human language.

LLMs are tools for communication, automation, and creative generation—and this is just the beginning of what AI can do.

With a better grasp of tokens, GPT, and transformers, you can now appreciate how AI generates intelligent, human‑like responses.

Next in the Series

• Deep Dive into Tokens, Embeddings, and Vector Search in LLMs — stay tuned for the next article!