>[!card] Training is where a large language model gains its facts, fine-tuning is for teaching specialized tasks and styles > "Although fine-tuning can feel like the more natural option—training on data is how GPT learned all of its other knowledge, after all—we generally do not recommend it as a way to teach the model knowledge. ==Fine-tuning is better suited to teaching specialized tasks or styles, and is less reliable for factual recall.== > > As an analogy, model weights are like long-term memory. When you fine-tune a model, it's like studying for an exam a week away. When the exam arrives, the model may forget details, or misremember facts it never read. > > In contrast, message inputs are like short-term memory. When you insert knowledge into a message, it's like taking an exam with open notes. With notes in hand, the model is more likely to arrive at correct answers." ([Question answering using embeddings-based search](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb))