How Large Language Models (LLMs) Work — A Beginner-Friendly Guide
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 allow humans to talk to computers using natural language instead of code, making it easier for anyone to interact with AI systems without learning programming.
2. How Are LLMs Trained?
LLMs are trained on massive datasets, including:
- Books
- Blogs
- Articles
- Code repositories
- Web content
Unlike databases, LLMs don’t store facts verbatim.
Instead, they learn patterns, relationships, and probabilities in language.
Think of it like humans learning a language — the more you read, the better you understand how sentences are structured and how to respond appropriately.
3. Tokens: How AI Understands Text
Computers don’t understand words — they understand numbers.
When you type:
“Hello world”
It might become something like:
[15496, 995]
This process is called tokenization, and it’s how LLMs convert text into something 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 the 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 AI predicts one token at a time and continues until a complete response is generated — similar to an advanced autocomplete system.
5. What Does GPT Mean?
GPT = Generative Pretrained Transformer
Breaking it down:
5.1 Generative
LLMs generate responses on the fly rather than searching the web.
Example:
You: “Call me Captain Dev”
LLM: “Sure, Captain Dev!”
This response is original, created by the AI based on patterns it learned — it didn’t exist anywhere before.
5.2 Pretrained
Before interacting with users, LLMs are trained on large datasets.
Like humans, they learn first, then generate content. This pretraining allows them to answer questions accurately and contextually.
5.3 Transformer
The transformer is the neural network architecture powering modern LLMs.
It allows the model to process context effectively and predict the next token accurately.
All major LLMs use transformer-based architectures:
- GPT (OpenAI)
- Gemini (Google)
- Claude (Anthropic)
- Mistral
In short, all these models are Generative + Pretrained + Transformers in nature.
6. How LLMs Generate Answers Step by Step
Think of an LLM as a super-smart autocomplete system:
- You type: “The sky is…”
- The model predicts: “blue”
- Then predicts the next token: “today”
- It continues predicting one token at a time until the full response is generated
This token-by-token generation allows LLMs to create long, coherent responses based on context.
7. Real-World Example
Let’s say you ask an LLM:
“Write a short introduction about yourself for a portfolio website.”
The process happens like this:
- Input: The AI receives your text (input tokens).
- Prediction: The model predicts the next word or token based on pretraining and context.
- Iteration: It continues token by token until the response is complete.
- Output: Detokenization converts the tokens into readable text for you to copy and use.
This is why AI can generate blog posts, code snippets, summaries, and more instantly.
8. Final Thoughts
LLMs are transforming 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.
- This is just the beginning of what AI can do.
With a better understanding 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!
Top comments (1)
Intresting