How AI and LLMs Work

Artificial Intelligence (AI) and Large Language Models (LLMs) like ChatGPT, Bard, or Claude are powerful tools that help generate text, answer questions, and assist in various creative and academic tasks. But understanding how they work doesn’t have to be overwhelming. This beginner’s guide will walk you through the basics, key concepts, and analogies to help you grasp how these systems function.

What is a Large Language Model (LLM)?

An LLM is a type of AI designed to understand and generate human-like text by analyzing massive amounts of data. These models are built using billions of words from books, websites, articles, and other sources to learn patterns and relationships between words. Essentially, they predict the next word in a sentence based on the input they receive.

Think of it as a highly advanced autocomplete system—similar to how your phone suggests words when you type a message. However, LLMs are far more complex, capable of generating coherent paragraphs, essays, poems, and even code.

Training Data: The collection of text used to teach the model, ranging from literature to websites.

Tokens: Small pieces of language (like words or word fragments) that the model processes.

Parameters: Adjustable parts of the model that help it generate accurate responses. LLMs like GPT-4 have billions of these.

Inference: The process of generating a response based on input from a user.

Fine-Tuning: Additional training on specific data to specialize the model for particular tasks (e.g., legal or medical advice).

How Do LLMs Work?

Imagine working with a friend who has read everything—encyclopedias, novels, research papers, and more. When you ask them a question, they don’t "think" the way you do, but they remember patterns and associations from everything they’ve read. When you give them part of a sentence, they try to guess what would make sense next based on all the patterns they’ve encountered.

This is how LLMs work:

Input: You ask the model a question or provide a prompt.
Tokenization: The input is broken into small parts (tokens).
Processing: The model analyzes the tokens and predicts the next part of the response based on its training.
Generation: It continues predicting words until it creates a complete, coherent response.

Common Challenges and Misconceptions

LLMs don’t truly understand context or meaning; they predict words based on patterns, which can result in confident but incorrect answers. They also reflect the biases present in the data they were trained on, which can introduce fairness issues. Additionally, models like ChatGPT have information cutoffs—they don’t have real-time access to current events unless connected to the web through plugins or APIs.

Tips for Using LLMs Effectively

I've made a page dedicated to AI Prompting here.

Extended Learning

Google AI Essentials: This course is designed for beginners and provides practical, hands-on experience with AI tools like ChatGPT and text-to-image generators. It focuses on topics like productivity enhancement, responsible AI use, and prompt engineering. The course is self-paced and takes about 10 hours to complete, with a certificate available upon completion through Coursera. Although the course costs $49, it’s structured to fit into busy schedules and requires no technical background (Grow with Google) (Coursera).

Google Cloud: They provide several short, practical courses on AI and generative models, such as "Introduction to Large Language Models" and "Generative AI Learning Path." These courses cover foundational concepts and are designed to be accessible even for those new to the subject. Some courses are free to audit, and certificates are available for a fee if you want to showcase your learning on platforms like LinkedIn (Google Cloud).

IBM SkillsBuild: IBM offers a variety of free courses that cover topics such as machine learning, generative AI, and natural language processing. The courses are flexible, with options to earn digital credentials upon completion, which can be shared on LinkedIn or resumes. IBM's focus is on making AI education accessible to learners of all levels, offering both foundational and advanced modules that cater to high school students, college students, and professionals (IBM) (IBM SkillsBuild).

Udacity: This platform offers in-depth programs like "AI Fundamentals" and more advanced topics such as "Deep Learning" and "Introduction to Machine Learning with TensorFlow." Many of these courses provide hands-on projects to reinforce learning and focus on practical applications, including natural language processing and reinforcement learning. Some courses are free, while others are part of Udacity’s nanodegree programs, which offer career support and personalized mentoring (Udacity).

Coursera: With courses like "AI for Everyone" by Andrew Ng and "Introduction to AI," Coursera caters to a broad audience, from beginners to professionals. Some of these courses are free to audit, and they also offer more structured learning paths with certificates upon completion (Coursera).