How Microchip Uses Memgraph’s Knowledge Graphs to Optimize LLM Chatbots

Microchip Technology, during a webinar hosted by Memgraph, showcased how integrating Large Language Models (LLMs) with knowledge graphs can optimize chatbot performance by employing Retrieval Augmented Generation (RAG). This integration allows LLM-powered chatbots to provide more context-aware and responsive interactions by grounding responses in structured, interconnected data, enhancing accuracy and reliability. A demonstration using examples from "Game of Thrones" illustrated how knowledge graphs enable LLMs to give more precise and detailed answers. Microchip's Senior Data Scientist, William Firth, explained the transition from theoretical applications to practical business solutions, highlighting a customer service chatbot that utilizes knowledge graphs to improve service efficiency and customer satisfaction. To address data privacy and integration challenges, Microchip developed a custom LLM tailored to their internal graph database, avoiding reliance on public APIs. The discussion also covered the importance of scalability in business environments and Memgraph's ability to handle extensive data without performance issues, allowing widespread implementation across various applications. During the Q&A, issues such as handling hallucinations and crafting intuitive graph models were addressed, emphasizing the significance of precise node and edge typologies for LLM performance.