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.
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.
Define the task before judging the answer. A fluent response can satisfy the shape of a request while missing a constraint that matters to the work.
- Distinguish model training from inference.
- Separate conversation context from stored history.
- Read the service’s documented data practices.
Follow a related question
Document units and representations.
Units travel with the columnDistinguish an amount from a rate.
Energy and power answer different questionsKeep learning
Related background to continue exploring this subject.
Google: an introduction to language models NIST: AI risk management framework
