Every token gets a long list of numbers: its coordinates on the map. Words with similar lists sit near each other. The model never learned a definition. It learned the neighbourhood.
Close means used together
No one tells the model where to put a word. It reads an ocean of text and nudges every word until words that keep company sit together. Dogs and puppies appear in the same stories, so they drift together. Money words form their own region.
Once meaning is distance, a question about meaning becomes a question about position. Which word fits here? becomes which points are nearest? A machine answers that fast.
king − man + woman ≈ queen
The map, drawn down to two
Touch any word. It lights up with its three nearest, and the caption names them. The clusters form on their own: animals with animals, food with food, money and work off in another corner.
Where you meet it
Every tool that promises to “read your files” runs on this. It turns each document into a point and stores the points. Your question becomes a point too. Then it fetches the nearest ones. No reading happens. It is a nearest-neighbour lookup, thousands of times a second.
What the map gets wrong
Two honest limits. The map is not a dictionary: “bank” the money place and “bank” the river share a point. And near is not the same as true: two sentences can sit close together and disagree completely. The map tells you what something sounds like. Never whether it checks out.
Working on this?
If AI is stuck somewhere in your business, tell me where. I read every one of these.