The need to train/tune a model, in this case LLMs, is assumed to rely on the requirement for grounding and running on the edge or offline. This need will vary b

The need to train/tune a model, in this case LLMs, is assumed to rely on the requirement for grounding and running on the edge or offline. This need will vary by use case. With log file analysis as an example, training a model may increase the model's ability to deal with outliers, through writing regex which is placed in the indexing pipeline. In this use, tuning a prompt isn't going to help much, given the foundation model might have no idea how to parse a given field in a log line no matter how you put it to it in the prompt. Tuning models also serves other purposes, such as removing guardrails introduced in the training data by others, and customizing the self referenced material the model "knows" about, such as its name, creators and the "personality" presented to the end user.