MedAI’s RAG Feature Accidentally Teaches Insurance Law to Frustrated Patient

Farhan Habib Faraz, an expert in AI design, encountered a significant issue with a knowledge base system that was too comprehensive for its own good. The problem arose when an AI system, designed to assist with patient appointments, provided excessive information to clients, causing confusion and frustration. On the second day of implementation, a patient requested an appointment, but the AI delved into detailed explanations of copays, insurance clauses, and coverage rules, prompting the patient to become overwhelmed. Faraz realized that the system's lack of context and role-based knowledge filtering was the root cause of the issue. By implementing a simplified conversation flow and teaching the AI to prioritize relevant information, Faraz was able to resolve the problem. The experience highlighted the importance of context and knowledge filtering in AI design, particularly in industries such as healthcare where clear communication is crucial. The solution has significant implications for the development of AI-powered customer service systems.