Understanding Large Language Models (LLMs) and Their Business Applications

Understanding GPT and Large Language Models (LLMs)

GPT (Generative Pre-trained Transformer) is a type of Large Language Model (LLM) capable of generating human-like text.
I’ve been using GPT in its various forms for years, and in this video, we’ll explore three key questions:

What is a Large Language Model (LLM)?

How do LLMs work?

What are the business applications of LLMs?

1. What Is a Large Language Model?

A Large Language Model (LLM) is a specific kind of foundation model—a model pre-trained on vast amounts of unlabeled, self-supervised data.
This means it learns directly from patterns and relationships within the data itself, without needing human labeling.

LLMs are foundation models designed specifically for text and text-like data such as natural language, code, or documentation.

These models are trained on massive datasets — books, articles, websites, and conversations.
“Large” refers not only to the amount of data but also to the size of the model itself.

To give you perspective:

A 1 GB text file can store about 178 million words.

A petabyte equals about 1 million gigabytes — an almost unimaginable scale of text data.

LLMs are also massive in terms of parameters, which are internal variables the model learns during training.
The more parameters, the greater the model’s ability to capture complex patterns in language.

For example:

GPT-3 was trained on roughly 45 terabytes of text data

It contains 175 billion parameters.

That’s what gives GPT-3 its powerful ability to understand and generate natural language.

1. How Do Large Language Models Work?

You can think of an LLM as having three main components:

Data – Huge volumes of text used for training.

Architecture – A type of neural network called a Transformer.

Training – The process through which the model learns language patterns.

The Transformer architecture allows the model to handle sequences of words by understanding the context of each word relative to every other word in a sentence.
This context-awareness is what makes LLMs so powerful.

During training, the model learns through next-word prediction.
For example, given the prompt:

“The sky is…”

At first, the model might randomly guess “bug.” But after many training iterations and adjustments, it learns that “blue” is the most likely continuation.

Over time, the model refines its internal parameters to minimize errors — developing an increasingly sophisticated understanding of language.

Once trained, an LLM can also be fine-tuned — that is, retrained on smaller, specialized datasets for specific purposes (like legal writing, coding, or customer support).
Fine-tuning transforms a general-purpose model into a domain expert.

1. Business Applications of LLMs

Large Language Models have a wide range of real-world business applications. Here are just a few examples:

Customer Service:
Powering intelligent chatbots that can handle common queries, allowing human agents to focus on complex cases.

Content Creation:
Assisting in writing articles, marketing copy, emails, social media posts, and even YouTube video scripts.

Software Development:
Supporting developers by generating, explaining, or reviewing code.

And that’s just the beginning.
As LLMs continue to evolve, they’re being integrated into industries ranging from healthcare and education to finance and creative arts — unlocking new ways to automate, assist, and innovate.

Final Thoughts

Large Language Models like GPT represent a major shift in how humans and machines interact through language.
Their ability to understand context, generate coherent text, and adapt to specific domains makes them incredibly versatile tools for the future.

If you’re as fascinated by LLMs as I am, leave a comment with your thoughts or questions below.
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Thanks for watching!