Generative AI Learning Series: Part 12 - Large Language Models (LLMs): The Technology Behind ChatGPT, Gemini, and Claude

Part 12 of the Generative AI Series

Welcome back to our Generative AI series! In Part 11, we witnessed the birth of Generative AI and how AI shifted from simply analyzing data to creating entirely new content. But one big question remains: How does a tool like ChatGPT actually answer your questions?

If the Transformer architecture we discussed in Part 10 is the “engine,” then a Large Language Model (LLM) is the complete vehicle. The engine provides the power, but the vehicle needs fuel, training, and navigation before it can take you anywhere.

1. What Exactly is a Large Language Model?

To understand how these AI assistants work, let’s break the term down into its three core components. These aren’t just buzzwords; they represent the fundamental architecture of the technology.

• Large: These models are trained on an incomprehensible amount of data. Imagine reading millions of books, research papers, websites, code repositories, and articles. The volume of text is vastly greater than what any human could read in a thousand lifetimes. They are also “large” because they contain billions (or even trillions) of parameters—internal mathematical values learned during training that capture the complex patterns of human language.
• Language: The model specializes in text. It learns grammar, sentence structure, vocabulary, writing styles, reasoning patterns, programming syntax, and translation. Note: It does not “understand” language the way humans do with feelings and consciousness; instead, it learns the deep statistical relationships between words.
• Model: A model is simply a trained AI system. Think of a chef. Before training, the chef knows nothing. After years of cooking, the chef develops deep expertise. Similarly, after processing mountains of data, the AI becomes a functional language model.

Putting It Together: A Large Language Model (LLM) is a deep learning model based on the Transformer architecture, trained on massive amounts of text to learn patterns in language and generate human-like responses.

2. Does an LLM Memorize the Internet?

This is one of the biggest misconceptions about AI. Many people assume ChatGPT works exactly like a search engine: Question ➔ Search Database ➔ Return Stored Answer

That is not how an LLM works. It doesn’t have a folder containing Wikipedia articles that it searches through.

2.1 The Librarian Analogy:

Imagine a new librarian joins a university. On Day 1, they know nothing. After reading thousands of books, they begin noticing patterns. If someone asks, “Can you recommend a book on AI?” the librarian doesn’t recite one perfectly memorized page. Instead, they combine knowledge gathered from many sources to give a synthesized, original answer.

Like a student studying for an exam, LLMs don’t memorize every page exactly. They learn concepts, patterns, relationships, and examples. They answer questions using this deep statistical understanding, rather than pulling from a giant database of pre-written answers.

3. How ChatGPT Actually Generates a Response

If LLMs don’t search a database, how do they write? The secret lies in predicting the next token.

Imagine reading this sentence:

The sun rises in the ____

Most people instantly think: east. Your brain naturally predicts the most likely next word based on a lifetime of learning English.

LLMs do exactly the same thing at a massive scale. They continually ask: “Given everything I’ve seen so far in this prompt, what is the most mathematically likely next piece of text?“

3.1 Token-by-Token Generation

Suppose you prompt an AI to write a sentence about India. The model does not generate the entire sentence at once. It generates it iteratively, one piece (token) at a time:

India ➔ is ➔ a ➔ diverse ➔ country ➔ with ➔ a ➔ rich ➔ heritage ➔ .

Each new word depends on all the words generated before it. This iterative loop continues until the model predicts a special “Stop” token, indicating the response is complete.

4. The Life Cycle of an LLM

An LLM doesn’t become intelligent overnight. It goes through a rigorous, multi-stage life cycle before it ever reaches your screen to answer a prompt.

1. Stage 1: Collecting Training Data - Researchers gather enormous amounts of publicly available text (books, encyclopedias, scientific papers, and code). The goal is to expose the model to the widest possible variety of human language.
2. Stage 2: Pretraining (Learning the Basics) - This is where the heavy lifting happens. The model learns general language patterns from massive datasets. Just as a child learns letters before quantum physics, the model learns vocabulary, grammar, facts, and logic by repeatedly guessing missing words across billions of sentences.
3. Stage 3: Fine-Tuning (Learning Specialized Skills) - After pretraining, the model knows language, but it isn’t necessarily a helpful assistant. Fine-Tuning provides additional training on carefully selected data to specialize the model. (Analogy: Everyone gets a basic education in school. Later, people specialize to become doctors or engineers).
4. Stage 4: RLHF (Learning Manners and Safety) - Reinforcement Learning from Human Feedback is what makes modern chatbots feel natural. Humans review the AI’s responses and rate which ones are clearer, safer, and more helpful. Without RLHF, AI might be technically accurate but rude or dangerous.
5. Stage 5: Inference (The Model is Ready) - Training is complete. Now, users begin asking questions. This stage is called Inference—the process of using the trained model to generate responses for new inputs.

5. Training vs. Inference

It is crucial to understand the difference between how an AI learns (Training) and how an AI answers (Inference).

6. Common Misconceptions

Understanding what an LLM is not is just as important as knowing what it is.

Did You Know?

Training a state-of-the-art LLM (like GPT-4 or Gemini) can take months and cost tens of millions of dollars using thousands of specialized computer chips (GPUs) running 24/7. But once trained, millions of users can interact with the model simultaneously during the Inference stage at a fraction of a cent per query!

9. Beginner FAQs

10. What’s Next?

You now understand how an LLM is built and trained. However, when using AI assistants, you will frequently hear specific jargon: What exactly is a token? What is a context window, and why do models sometimes “forget” earlier parts of a long conversation? Why do LLMs hallucinate?

In Part 13, we will explore these practical, everyday concepts—the essential vocabulary every AI user needs to master tools like ChatGPT, Gemini, and Claude effectively.