Training adjusts a model using a chosen process and collection of examples. Inference uses a trained model to produce a result from an input. These stages answer different questions about how the system works.
For a typical deployed language model, the current conversation supplies context for a response rather than automatically becoming a training step. A service may separately have data-handling or improvement practices, which need to be read in its own documentation.
Keep those distinctions clear when asking what the tool knows or remembers. Model behavior, conversation storage, and a provider’s use of submitted information are related parts of the service, but they should not be treated as one mechanism.
- Distinguish model training from inference.
- Separate conversation context from stored history.
- Read the service’s documented data practices.
Picture this situation.
Consider instructions supplied for one drafting session. They guide that interaction without necessarily changing the model’s underlying training.
A second way to look.
A small trial should have a clear stopping point. Decide which uncertainty the tool can help explore and what observation would answer the next question.
Follow a related question
Know which account or profile you are using.
A shared device needs a clear endingSeparate time-sensitive information from routine updates.
Notifications with a reason to arriveKeep learning
Related background to continue exploring this subject.
Google: an introduction to language models NIST: AI risk management framework
