What technique is most appropriate for answering questions based on information found in emails?

The technique of using Retrieval-Augmented Generation (RAG) is most appropriate for answering questions based on information found in emails because it combines the strengths of both retrieval and generation. In this approach, the large language model (LLM) can access a database of emails to retrieve relevant information before generating a response. This allows the model to provide accurate and context-aware answers based on the specific content of the emails.

By retrieving the most pertinent emails, the model ensures that the generated responses are grounded in the actual data, which enhances the accuracy and relevance of the answers given to user queries. This method is particularly beneficial for handling the vast and nuanced information typically found in emails, allowing for a more informed and tailored interaction.

Other techniques, while potentially useful in different contexts, do not provide the same level of reliability for this specific task. For example, fine-tuning an LLM on your emails could work, but it requires a significant amount of labeled data and time, and it may not capture the full range of context as effectively as RAG. Prompting without RAG could lead to less accurate responses, as the model would lack access to the precise content of the emails when generating answers. Pretraining an LLM on your emails is also less