Training vs Inference

Training updates a model's parameters using data. Ordinary inference uses existing parameters to produce an output. Changing the conversation context is different from updating model weights.

Follow one example

Try the linked short lesson above to compare these situations. A better next reply does not by itself prove that training occurred.

Training can happen in stages

Pretraining builds an initial model. Further training can adapt it to instructions or another task. Training is not necessarily a single event, and some approaches update only a subset of parameters or additional adapters.

Hugging Face explains pretraining and fine-tuning.

Inference can involve several steps

Generating text usually involves repeated prediction and decoding steps. Implementations can reuse cached computation, such as a KV cache. Inference does not ordinarily update model weights, but it can change temporary state and application records.

Applications may bring earlier messages, saved preferences or retrieved documents into the next request. Saying “no parameter update” does not mean “nothing is retained.” See Anthropic’s model, tools and memory explanation.

What this means for your work

• For information needed in one answer, provide relevant context or retrieve it.
• To change recurring model behavior, evaluate whether training is appropriate; it requires data and validation.
• Cost depends on model size, data, output length, hardware and request volume. Neither activity is always cheap, and total costs cannot be inferred from the label alone.
• Whether conversation data is stored or used for later training depends on the product’s policies and settings. Ordinary inference alone does not answer that question.

Remember: distinguish parameter updates, context, temporary caches and product storage.

Next: What happens during pretraining?