How Generative AI Works: The Basics Explained

Welcome back! Now that we understand the difference between Machine Learning AI and Generative AI, let’s take a deeper dive into how Generative AI actually works. You might be wondering—how can AI generate text, create images, or even write code? It seems like magic, but there’s actually a very structured process behind it.

Imagine Generative AI as a master storyteller. Think of it like a person who has read every book, watched every movie, and listened to every conversation ever recorded. When you ask it a question, it doesn’t just copy something it’s read before—it pieces together patterns, words, and ideas to craft something entirely new. It’s not just remembering facts; it’s creating a response based on probabilities and learned relationships.

So how does it do this? It all comes down to something called Large Language Models, or LLMs.

The Recipe for Generative AI: How Large Language Models Work

Let’s go back to the kitchen metaphor. If Machine Learning AI is like a chef following a recipe, then Generative AI is like a chef who has read every cookbook ever written and uses that knowledge to invent new dishes.

But here’s the key—Generative AI doesn’t just randomly throw ingredients together. It looks at everything it has learned and predicts the most likely next step. Just like a chef knows that tomatoes and basil go well together, Generative AI knows that certain words, phrases, and ideas are commonly associated with each other.

These models are built using Large Language Models, or LLMs. LLMs are trained on billions of words, phrases, and concepts from books, websites, and other publicly available data. The AI learns patterns, structures, and meanings, so when you give it a prompt, it predicts what should come next based on what it has seen before.

Think of it like the autocomplete feature on your phone. When you start typing, "Hey, how are…", your phone suggests "you?" because it has learned that those words usually follow each other. Generative AI does the same thing—just at an incredibly advanced level.

Who’s Building These AI Models?

Right now, a few key companies are leading the way in building the foundational models that power Generative AI. These are the organizations that are training massive AI models and making them available for businesses and developers to use.

• OpenAI – They built ChatGPT, which is one of the most widely used generative AI tools today. Their models, like GPT-4, are designed to process and generate human-like text.

• Google DeepMind – Google’s AI division, which has developed models like Gemini (formerly Bard).

• Anthropic – The creators of Claude, another AI language model focused on safety and high-quality responses.

• Meta (Facebook) – They are developing AI models like LLaMA (Large Language Model Meta AI), which are open-source and designed for research and enterprise applications.

• Mistral AI – A newer player focused on powerful, open-source AI models.

• Cohere, Stability AI, and Hugging Face – These companies are building specialized AI tools, including image and language generation models.

Each of these companies is pushing AI forward, training their models on different data sets, using different techniques, and optimizing for different use cases.

What Happens When You Type a Prompt into Generative AI?

Let’s break it down step by step.

1️⃣ You give it a prompt. Let’s say you type, "Write a funny story about a cat who becomes a CEO."

2️⃣ The AI breaks down your request into tokens. A token is just a small piece of a word or phrase—kind of like how syllables work in language.

3️⃣ It analyzes patterns from everything it has learned. The AI looks at thousands of stories, jokes, and business concepts it has been trained on.

4️⃣ It predicts what should come next. Based on all its training, it calculates that the most likely next words should be something like, "Once upon a time, a curious orange cat named Whiskers found himself in an executive boardroom…"

5️⃣ It generates a response word by word. The AI isn’t copying a story it has read before—it’s predicting each word, one at a time, based on probabilities.

This entire process happens in seconds, thanks to the massive computational power behind these AI models.

Why Does Generative AI Feel So Human?

Because these models are trained on human conversations, books, and articles, they’ve learned how humans communicate. The secret is in how they structure responses—matching tone, context, and style.

For example:

• If you ask it for a formal business email, it will structure it in a professional way.

• If you ask for a joke, it will match the rhythm and humor of real-world jokes.

• If you ask for a poem, it will follow poetic structures like rhyme and meter.

Generative AI doesn’t think the way we do—it just predicts patterns so well that it feels natural.

Beyond Text: AI is Generating More Than Just Words

Generative AI isn’t just about text. The same principles apply to AI models that generate images, code, music, and even videos.

• DALL·E can create images from text descriptions.

• Midjourney and Stable Diffusion generate high-quality artwork.

• GitHub Copilot helps programmers by suggesting code.

• Runway AI is creating AI-powered video editing tools.

These tools all use the same foundational concept—predicting the most likely next element based on learned patterns.

The Challenges of Generative AI

Of course, Generative AI isn’t perfect. It can sometimes generate incorrect or biased information because it is only as good as the data it was trained on. It also doesn’t truly understand meaning—it’s just predicting what’s most likely to make sense based on patterns.

That’s why human oversight is crucial. AI is a powerful tool, but it still needs critical thinking and fact-checking to be truly useful in business.

Why This Matters for Business

Now that you know how Generative AI works, let’s talk about why it’s important for businesses.

Understanding how these models function allows you to: ✅ Use AI more effectively by knowing its strengths and limitations. ✅ Write better prompts to get higher-quality responses. ✅ Stay ahead of AI trends and leverage new tools as they emerge.

Generative AI is changing marketing, customer service, content creation, and automation—but the businesses that truly succeed will be the ones that understand how to use it strategically.

Lesson Takeaways

Generative AI uses Large Language Models (LLMs) trained on vast amounts of text and data.

It generates content by predicting what should come next, rather than recalling exact information.

Big players like OpenAI, Google, Meta, and Anthropic are leading the AI revolution. AI is not perfect, and human oversight is key to using it effectively in business.

That’s it for this lesson! In our next session, we’re going to look at how AI has evolved over generations—how we got from early AI systems to today’s mind-blowing generative models. Stay tuned!