Organizations globally are leveraging the capabilities of Large Language Models (LLMs) to enhance their chatbot functionalities. These advanced chatbots are envisioned not just as tools for basic interaction but as sophisticated systems capable of intelligently accessing and processing a diverse array of internal organizational assets. These assets include detailed knowledge bases, frequently asked questions (FAQs), Confluence pages, and a myriad of other organizational documents and communications.
This strategy is aimed at tapping into the rich vein of internal knowledge, ensuring more accurate, relevant, and secure interactions. However, this ambitious integration faces significant hurdles, notably in the realms of data security, privacy, and the avoidance of erroneous or "hallucinated" information, which are common challenges in AI-driven systems. Moreover, the practical difficulties of retraining expansive LLMs, considering the associated high costs and computational requirements, further complicate the situation. This article delves into a strategic solution to these challenges: the implementation of Retrieval-Augmented Generation (RAG) models in conjunction with LLMs, complemented by the innovative use of session-based context management through Redis cache.