Inference, within the context of generative AI, refers to the process where a trained model, like those used in artificial intelligence, generates new content based on the patterns and data it has previously learned. This is akin to an artist who, after practicing and observing many landscapes, can create a new, unique painting that captures the essence of a landscape without directly copying any specific scene.

Imagine a generative AI as a treasure chest filled with jewels of knowledge gathered from various maps (data). When you ask this treasure chest for a new jewel (output), inference is the magic that rummages through the chest, combining pieces of what it knows to produce a new gem that fits your request. This new gem could be anything from a piece of text, an image, music, or even a video that didn't exist before but is created based on the knowledge the AI has acquired.

For example, if you've trained your AI on a library of pirate tales, inference is when you ask it to tell you a new story, and it crafts one for you. It's not just retelling a tale it has memorized; it's using its understanding of pirate stories' structure, themes, and characters to generate something new and original. This process doesn't require the AI to go back to its training data; instead, it uses the model it has built from that data to generate these new treasures on demand.