Q: 6
You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in
retrieving relevant documents from a vast knowledge base. The first step in this process is to generate
vector embeddings for the documents using a pre-trained model. After generating embeddings, you
notice that the model is sometimes failing to retrieve semantically similar documents. Which of the
following is the most appropriate approach to ensure that semantically similar documents are retrieved
effectively?
Options
Discussion
Fine-tuning on your own data would help, so D. The others don't really address semantic similarity. Not totally sure though.
Does the question specify if there's access to a task-specific dataset? If not, then B could make sense for resource constraints, but if domain adaptation matters most then the answer would flip to D.
Its D fine-tune the model. Only way to really boost embedding quality for semantic retrieval in your specific use case.
Its D. Fine-tuning will boost semantic similarity retrieval for your specific domain. The others miss the real issue here.
Be respectful. No spam.
