#📚reference ## What Generative AI Actually Means You've heard the buzz. Everyone talks about generative AI like it's magic. It's not magic — it's something better. It's a tool that understands patterns and creates new things from what it learned. Think of it this way: you show a child thousands of cat photos. Eventually, they can draw a cat without copying any specific photo. That's generative AI in action. > [!quote] According to [Wikipedia](https://en.wikipedia.org/wiki/Generative_artificial_intelligence), generative AI is artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data with similar characteristics. ## How is it Different Traditional AI predicts things. "Is there a face in this photo?" Generative AI creates things. "Draw me a face that looks like it belongs in this photo." Here's a simple example. You want to know if an image contains a human face. You need a predictive model. It learns to match pixel patterns to "yes" or "no" answers. But what if you want something new? You want your model to create faces that look real but never existed. Now you need a generative model. It learns how faces work—not just how to recognize them. > [!tip] Predictive vs. Supervised > Don't confuse predictive modeling with [supervised learning](https://en.wikipedia.org/wiki/Supervised_learning). One defines what the model does. The other defines how it learns. You can teach a generative model using supervised techniques. You can also predict things without supervision. ## How Similarity Works Generative AI relies on a simple idea: similarity. But how do we measure it? Imagine organizing your music library. Rock songs go near other rock songs. Jazz stays with jazz. But where do you put rock-jazz fusion? Somewhere in between, right? AI does the same thing. It maps everything into an invisible space where similar things cluster together. The Cheshire Cat sits closer to other cats than to Descartes. But it's not far from Cat Woman — they're both fictional characters. > [!tip] For those interested in mathematics, this "distance" is properly called a [metric](https://en.wikipedia.org/wiki/Metric_space). Not everything can be consistently measured this way, but it works well enough for most practical purposes. ## The Magic Space Inside AI Every generative model creates an internal map. We call this a [latent space](https://en.wikipedia.org/wiki/Latent_space) or an [embedding](https://towardsdatascience.com/what-is-embedding-and-what-can-you-do-with-it-61ba7c05efd8). Think of it as a library catalog with infinite categories. This space lets us do interesting things: - Walk through it guided by your input - Find the middle ground between a cat and a human - Add randomness to spark creativity - Discover patterns we never knew existed Some categories make perfect sense (level of "catness"). Others remain mysterious. The model finds patterns in data that humans never considered. ## Your AI Librarian Here's a good way to think about generative AI: > [!quote] Generative AI is a knowledge base with a friendly user interface. Picture a librarian who has read every book in the world. They can't quote exactly from memory. Sometimes they mix up details. But they understand the big picture and can help you explore ideas you never knew you wanted. This explains why people use ChatGPT instead of Google. It doesn't just find information — it helps you think through problems. Most successful generative AI applications work this way. They take vast knowledge and make it accessible through conversation. ## What This Means for Your Business The architecture of modern AI models reveals something important. If your problem involves summarizing knowledge or creating content from patterns, generative AI can help. The key is matching the tool to the task. Not every business problem needs AI. But when you need to process information, generate content, or understand patterns, generative AI excels. > [!tip] We help you harness generative AI > Through [[research|AI-powered product research]], strategic [[plan|planning that optimizes for AI-assisted development]], and [[build|expert-led building with AI-enhanced workflows]]. > > Whether you're exploring new opportunities, defining roadmaps, or developing products — we believe in human-first AI that amplifies what you already do well. The future belongs to businesses that use AI thoughtfully. We're here to help you navigate that journey with confidence and clarity. --- <font style="color: #F86759">Contributors:</font> *[[Mykhailo]], [[Jaros]]* <font style="color: #F86759">Last edited:</font> *2025-06-10*