Skip to content

What is an LLM and How Does It "Think"?

Objective: To establish a practical, non-technical mental model of a Large Language Model (LLM). By the end of this introduction, you will be able to define what a model is, explain the leap from simple statistical models to LLMs, and understand the core business value proposition of Generative AI.

Let's Start with a Familiar Model

Before we dive into the complexities of AI, let's start with a concept that should be familiar: the linear equation y = mx + b.

This simple equation is the foundation of a linear model, a workhorse of statistical analysis and machine learning. A model is, at its core, a simplified representation of a real-world phenomenon. In this case, the model uses just two parameters—m (the slope) and b (the intercept)—to describe the relationship between an input x and an output y. Despite its simplicity, this model has been used for decades to represent complex phenomena, facilitate analysis, and make predictions. The inputs and outputs are numerical, and the model's "knowledge" is contained entirely within those two parameters.

From Lines to Language: The Leap to LLMs

Now, imagine a model that doesn't work with simple numerical inputs but with the rich, complex, and nuanced data of human language. This is the domain of a Large Language Model (LLM).

While a linear model uses two parameters to model a line, an LLM uses billions of parameters to model the intricate patterns, grammar, context, and knowledge embedded in natural language.

  • If m and b are two tuning knobs for a linear model, an LLM has billions of microscopic tuning knobs that have been automatically adjusted during its training process.

This is the fundamental difference in scale and function:

  • A linear model takes numerical features as input and predicts a numerical target.
  • A Large Language Model takes a context as input—which can include text, images, audio, or video—and its predicted output is newly generated content, such as a human-like text response.

This ability to create new content is why this technology is called Generative AI. The LLM is the underlying engine, and Generative AI is the broad category of applications it powers.

When you interact with a Generative AI application, the response may appear to be thoughtful, intelligent, or even emotional. However, it's crucial to remember what's happening under the hood. The generated content is a statistical prediction, not a product of conscious thought. Its quality is a function of three things:

  1. The patterns it learned from its massive training data.
  2. How it was fine-tuned to respond to specific instructions.
  3. How it was aligned with human preferences using feedback and reward models.

The Power and Peril of the Data: Modeling Human Language

It's tempting to think of the vast amount of text data used to train an LLM as just a collection of words. However, it's essential to remember what human language truly represents. It is not just a means of communication; it is a repository of our collective understanding and misunderstanding of the world.

This training data contains:

  • Our Knowledge: Centuries of scientific discovery, history, literature, and practical know-how.
  • Our Flaws: Our societal biases, stereotypes, historical injustices, misinformation, and blind spots.

When an LLM is pre-trained on a dataset that reflects this human-generated content, it learns to replicate the patterns of everything—our greatest insights and our deepest flaws.

This simple fact is the source of both the immense power and productivity of these AI tools and the profound need for caution in how we build and use them. The model's ability to draft a brilliant marketing plan comes from the same place as its potential to generate a biased or harmful response. This is why the model development process cannot stop at just absorbing data; it must be followed by careful tuning and alignment.

The Business Value: From a Portfolio of Specialists to One Generalist

Before the rise of LLMs, companies relied on a wide range of specialized machine learning models to meet business needs. A firm might have:

  • One model to analyze customer reviews for sentiment.
  • Another model to recommend products based on purchase history.
  • A third model to detect fraudulent transactions.

Each of these required dedicated teams of data scientists and engineers to train, deploy, and maintain this portfolio of specialist models.

The core value proposition of an LLM is that a single, general model can replace a broad swathe of these specialized systems. Instead of building a new model for each task, a business can now instruct one powerful model "on the fly" using natural language. The skill of crafting these instructions is called Prompt Engineering. This consolidation promises to dramatically reduce complexity and accelerate the deployment of AI-powered solutions.

How is an LLM Built? A Glimpse into the Process

The incredible capabilities of models from leading AI labs like Google DeepMind, OpenAI, and others are the direct result of a multi-stage model development process. In this course, we will explore this process in detail, but at a high level, it consists of:

  1. Pre-training: The foundational stage where a model learns general knowledge and language patterns from a vast dataset.
  2. Supervised Fine-Tuning (SFT): The model is trained on a smaller, high-quality dataset to specialize it for specific tasks, like following instructions.
  3. Alignment and Safety Tuning: The model's behavior is further refined using human feedback to ensure its outputs are helpful, harmless, and aligned with ethical principles.

A model's final capabilities—whether it can write code, understand images, or generate marketing copy—are a direct outcome of the choices made during this development process. Understanding this lifecycle is key to evaluating, selecting, and managing AI models in a business context.