What typical problems do RAG (Retrieval-Augmented Generation) have?

RAG (Retrieval-Augmented Generation) is a method that combines generative models with knowledge base search. Typical problems of RAG:

• Search quality: if the search returns irrelevant documents, the generation will be erroneous.
• Knowledge base update: the need to regularly update the index to keep information current.
• Fact control: the generative model may "hallucinate" facts even when relevant documents are available.
• Integration complexity: combining search and generation requires fine-tuning and balancing.
• Performance: search and generation can be resource-intensive, affecting response speed.

To address these issues, improvements are made in indexing quality, result filtering, training the model on specific data, and infrastructure optimization.