Starter guide
What Is LLM Fine-Tuning?
Fine-tuning is an extra round of training for a model that has already learned language. You give it a smaller set of examples that show the task and the kind of answer you want. Training adjusts the model so it is more likely to respond that way in the future.
Imagine you want a model to turn meeting notes into a short list of decisions and action items. Your dataset would contain meeting notes paired with good summaries. By learning from those pairs, the model becomes more reliable at producing the same kind of summary for new meetings.
When is fine-tuning useful?
Fine-tuning is useful when you need the same behavior across many requests. It can help a model follow a fixed format, use a consistent tone, or perform one repeated task more reliably.
Fine-tuning does not give a model access to new information. If an answer depends on current documents or data, provide that information when the request is made.