The More We Chat, the Better It Knows Me — Is It Learning?
After using AI for a while, you may have felt this: it knows where you live, what you like to eat, and gets more thoughtful the more you chat — as if it's slowly getting to know you. Is it really learning and evolving? The answer is a bit of a surprise: it hasn't learned a single word.
It is not learning. Chat doesn't change the model itself — its brain was frozen the moment it left the factory. What feels like knowing you better is the product writing your details in a little notebook and quietly stuffing them back in before each conversation. Like a forgetful old friend who flips through their notes before they see you.
A large model's life has two stages: training and chat. Training is like school — it reads huge amounts of material and keeps adjusting the parameters in its head. Chat is like going to work after graduation — it only uses what it already learned. The key: from the day it started the job, those parameters were locked in. Chat with it for ten thousand turns and not one of them will move. For a detailed breakdown of the two stages, this site has a deeper lesson on training vs. use.
On "it knows me better and better," the picture in most people's heads is a long way from what actually happens. Tap the three buttons below and look at them one by one.
That little notebook in "what actually happens" is the "memory" feature every product advertises. It's a notepad hung outside the model: key details you mention in chat (where you live, what you eat, what you do) get pulled out and stored. Before each new conversation, the product pastes those items at the very front of the chat. The model reads them, so the answer naturally sounds like it knows you. The space those pasted items take is the context window.
This mechanism also explains a common frustration: switch products, and the memory doesn't come with you. The little notebook lives on product A's servers; product B can't see it. Years of rapport you've built on one side reset to zero the moment you switch apps. So for important personal preferences, keep a copy in a document of your own — paste it wherever you go.
Training one model per person is insanely expensive
Training a large model once means thousands of specialized chips running for weeks, at a cost in the hundreds of millions. Training a private model for each of hundreds of millions of users is a bill no company can pay.
A little notebook is cheap and good enough
By comparison, giving each user a notepad costs almost nothing. You won't feel the difference: it still knows you live in Guangzhou, eat vegetarian, and have a cat.
Frozen is actually safer
If anyone could rewrite the model just by chatting, some people would teach it good things — and some would teach it bad ones. Frozen parameters keep it at the same level for everyone. That's a feature, and a safeguard.
✅ What this page wants to share with you
- Model parameters are frozen at the factory: no amount of chat will change its brain
- "Memory" is a bolted-on little notebook: the product stores your details and stuffs them back in before each conversation
- When it feels like it knows you better: credit the product design — the model is just reading along
- Keep a copy of important preferences yourself: the little notebook resets when you switch products; bring your own doc and paste it wherever you go