eGain Identifies Seven Causes of AI Chatbot Errors

eGain Corporation, a specialist in enterprise customer service, has released an analysis detailing seven key causes behind inaccurate responses from AI-powered chatbots. While it's often instinctual to blame the AI model itself for incorrect information, the company's analysis suggests the real issues typically lie within the underlying knowledge base and its management.

The analysis points to common error sources such as outdated or missing information, content inconsistencies and ambiguities, and weak retrieval mechanisms. Furthermore, AI models' tendency towards 'hallucination,' where they generate fabricated information, and a lack of continuous optimization can also lead to errors in customer service interactions.

eGain suggests that resolving these issues rarely requires new or more advanced models. Instead, the focus should be on strengthening reliable, managed knowledge bases. The company's proposed solutions center on the quality of the knowledge base: ensuring content is up-to-date, comprehensive, and structured. Robust retrieval is also emphasized to guarantee the chatbot uses only company-approved information when responding.

Additionally, eGain underscores the importance of continuous optimization. This involves ongoing analysis of customer interactions, identifying and filling knowledge gaps, and eliminating contradictory information. This process ensures that the customer service AI remains accurate and trustworthy, consistently providing customers with correct information.