Skip to main content

Lesson 3: What is a Large Language Model?

Topics Covered
  • What a Large Language Model (LLM) is.
  • How LLMs work (Data, Architecture, Training).
  • The difference between pre-training and fine-tuning.
  • Real-world business applications.

GPT (Generative Pre-trained Transformer) is a Large Language Model (LLM) that capable of generating human-like text. But what essentially is it, and how does it drive business value?

1. What is an LLM?​

A Large Language Model is an instance of a Foundation Model applied specifically to text and code.

  • Foundation Models: Pre-trained on vast amounts of unlabeled, self-supervised data. They learn patterns in a way that produces generalizable, adaptable output.
  • Scale of Data: These models are trained on petabytes of text (books, articles, conversations).
    • Perspective: A 1GB text file can store about 178 million words. An LLM might be trained on millions of gigabytes.
  • Parameter Count: A parameter is a value the model changes internally as it learns. More parameters generally mean more complexity and capability.
    • Example: GPT-3 is pre-trained on nearly 45 terabytes of text data and uses 175 billion parameters.

2. How do they work?​

You can think of an LLM as consisting of three key components: Data, Architecture, and Training.

The Architecture: Transformers​

The core architecture is a neural network called a Transformer.

  • It handles sequences of data (like sentences or code).
  • It understands the context of each word by considering it in relation to every other word in the sequence.
  • This allows it to build a comprehensive understanding of sentence structure and meaning.

Training: Predicting the Next Word​

During training, the model learns a simple task: predict the next word.

  1. Input: "The sky is..."
  2. Initial Guess: "Bug" (Random).
  3. Correction: The model adjusts its internal parameters to reduce the error.
  4. Result: Eventually, it learns to predict "Blue".

By repeating this billions of times over enormous datasets, the model learns reliable, coherent language generation.

Fine-Tuning​

After general pre-training, the model can be fine-tuned on a smaller, specific dataset. This allows a general model to become an expert at a specific task (e.g., medical diagnosis or coding assistance).

3. Business Applications​

LLMs are transforming multiple industries:

  • Customer Service: Intelligent chatbots handle complex queries, freeing up human agents for critical issues.
  • Content Creation: Generating articles, emails, social media posts, and video scripts.
  • Software Development: Generating boilerplate code, writing unit tests, and reviewing existing code.

As these models evolve, we are only scratching the surface of their potential applications.