Structuring Content for Retrieval-Augmented Generation Chatbots: An Analysis of Current Best Practices

Abstract

Background. As generative Artificial Intelligence (AI) tools become more popular, information-seeking behaviors are shaping how businesses produce content and how users access information. However, mass-market chatbots like ChatGPT are prone to producing inaccurate information due to their broad training data. Objective. My research aimed to identify broadly recognized best practices for structuring content to enhance the effectiveness of Retrieval-augmented Generation (RAG) chatbots. Methods. To find practical information on RAG chatbots, I analyzed source content from practitioner spaces, including Software as a Service (SaaS) blogs and professional conference materials, focusing on broadly recognized recommendations for structuring content for RAG models. Results. I analyzed 16 unique practitioner sources and coded recurring themes into seven best practice heuristics for structuring content to improve RAG chatbot performance. Conclusion. While no universal standards exist yet for structuring content for RAG chatbots, my research identified overlapping best practices that can guide the implementation of RAG chatbots with an organization’s documentation source. To be successful, organizations must test and adapt these strategies to their specific content and remain committed to ongoing performance monitoring.