Does the model learn from my conversation?

Not during the conversation. Weights do not change at inference. Providers may later use collected conversations to train a future version, which is a separate decision governed by their data settings, not something happening live in your chat.

Why does training need so much more hardware?

Training processes vast batches and has to keep gradients and optimiser state for every weight, several times the size of the model itself. Inference only reads the weights forward. That is why training runs on large clusters for months and one answer runs on a fraction of that.

What is fine-tuning, then?

A small, late round of training on a specific dataset, done after the expensive pretraining. It changes weights, so it is training, but at a scale a small team can afford. Once it finishes, using the result is inference again.

Plain-language explainer

Training and inference, the two halves of AI

What is the difference between training and inference?

Training is where the numbers are learned: the model reads enormous amounts of text, gets things wrong, and every weight is nudged to be less wrong. It happens once, costs a fortune, and then stops. Inference is every time you use the result: the weights are frozen and read, not changed, to turn your prompt into an answer. Nothing you type during inference is learned. That single fact explains why a model does not know today's news, why it forgets your last conversation, and why answers are billed per token.

Last reviewed July 25, 2026

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What people get wrong

The model keeps learning from your chats. Weights are frozen at inference. Anything it seems to remember was put back into the prompt by the product.
Training is the expensive part. It is a huge one-off, and for a popular product the accumulated cost of serving answers passes it.
A model can just be updated with new facts. Changing weights means a training run; that is why current information arrives through retrieval and tools instead.

Where you see it in real products

A knowledge cutoff date in the model's own description.
Per-token API pricing, which is a price on inference only.
A chat that forgets you between sessions unless the product saves notes and pastes them back.

Common questions

Does the model learn from my conversation?: Not during the conversation. Weights do not change at inference. Providers may later use collected conversations to train a future version, which is a separate decision governed by their data settings, not something happening live in your chat.
Why does training need so much more hardware?: Training processes vast batches and has to keep gradients and optimiser state for every weight, several times the size of the model itself. Inference only reads the weights forward. That is why training runs on large clusters for months and one answer runs on a fraction of that.
What is fine-tuning, then?: A small, late round of training on a specific dataset, done after the expensive pretraining. It changes weights, so it is training, but at a scale a small team can afford. Once it finishes, using the result is inference again.

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