Retraining LLMs is costly both in time and resources. But when dealing with specific domain problems that the LLM hasn't encountered in its training set it can be difficult to get deep and meaningful results.

A better usage of pre-trained LLMs, in this case, is Retrieval Augmented Generation or RAG. RAG references a knowledge base outside of its training data before creating responses. This means it can reference your personal data and better tailor its responses to your specific use case.