Action Research to Enhance Enterprise-Specific Chatbot (ESCB) Security & Performance
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Abstract
Previously, we conducted three action research cycles to develop a chatbot customized to answer questions particular to the chatbot-creating organization. This enterprise-specific chatbot (ESCB) creation technique uses a corpus of local policy documents (CLPD) as a knowledge base, readily available software tools, a basic level of programming competence, and user community feedback. The ESCB development process leverages the power of Artificial Intelligence (AI), Natural Language Processing (NLP), and proprietary local data to transcend some of the typical limitations of conventional chatbots. Utilizing two additional action research cycles, we evolved the ESCB to improve resource utilization and prevent some forms of misuse. Using new advancements for context window size, we included more information within each query, lexical complexity analysis of user queries, and a large language model (LLM) firewall (FW). This work continues to underscore the significant potential of AI-powered chatbots for data interaction and the affordability of AI implementation, paving the way for organizations with limited resources to leverage the power of AI in their own local operations.
