Mandar Kulkarni and his team explore RAG optimization for FAQ chatbots using Reinforcement Learning in their insightful research.

Summary:

• LLMs trained for domain-specific conversational tasks can be further enhanced with Retrieval Augmented Generation.
• The in-house retrieval model using infoNCE loss surpasses general-purpose models in accuracy and Out-of-Domain detection.
• A proposed policy-based RL model interacts with RAG to optimize the cost by deciding whether to fetch or skip retrieval.
• RL optimization combined with a similarity threshold achieves cost savings with a slight accuracy improvement.

This RL-based approach presents a valuable methodology for enhancing domain chatbots, potentially reducing operational costs while maintaining performance, which is crucial for the widespread deployment of intelligent conversational agents.