Data in the instructions, or system prompt, that are delivered to the model is the simplest way to enable interactions between a model and your data
Model outputs can be made sure to be firmly based on your data by using retrieval augmented generation, or RAG
You may wish to think about SFT LLM, also known as Parameter Efficient Fine Tuning (PEFT)
You must give the model input-output pairs to learn from in order to execute supervised fine tuning
A method called Reinforcement Learning from Human Feedback, or RLHF, builds a model that is strengthened by human preferences and tailored to your particular requirements
Distillation is a brilliant technique that combines two objectives: reducing the size of the model so that it can handle data more quickly and making it more task-specific
Consider the scenario where you wish to use a smaller model to double-check every email you send in order to make them seem more formal
Another advantage of RAG is that, depending on who is calling the model, you can manage who has access to what grounding data