What is a recommended combination of techniques for a health tech company addressing inaccuracies in a generative AI chatbot?

The combination of chain-of-thought reasoning and retrieval-augmented generation (RAG) with grounding in internal health records is particularly effective for addressing inaccuracies in a generative AI chatbot, especially in the health tech domain.

Chain-of-thought reasoning encourages the model to break down problems into smaller, logical steps, which enhances the accuracy of the responses by allowing it to reason through the query in a more human-like manner. This method can significantly improve the quality of interactions by making sure the chatbot considers multiple relevant aspects of a question before delivering an answer.

Retrieval-augmented generation, on the other hand, involves incorporating external information sources, like internal health records. This grounding provides accurate, up-to-date data for the chatbot to reference, which is crucial in a healthcare context where information is constantly evolving. By leveraging authoritative and context-specific sources, the chatbot can deliver more reliable and precise information, thus reducing the likelihood of inaccuracies.

Combining these techniques allows for a more robust and trustworthy chatbot design, directly addressing the need for accuracy in health-related inquiries. This is essential in ensuring users receive credible advice and information, which can significantly impact their health decisions.