Are All AI Models Secretly Speaking the Same Language? As I’ve been digging into how generative AI actually works under the hood, I ran into something that took me a while to wrap my head around. LLMs turn words and phrases into something called embeddings — basically points in a high-dimensional space called “latent space”. Once everything is mapped into this space, the model can measure how similar things are using something called cosine similarity . The closer two embeddings are, the more related they are. As you would expect, ‘dogs’ and ‘cats’ end up close together but so do more complex relationships such as “doctor” and “hospital” where the model capture not only physical similarities but also functional similarities. So far, that makes sense. But then I came across something that completely changed how I think about this space. You can subtract and add concepts together. For example, if you take the embedding for “king” , subtract the embedding for “man” , and then add “woma...