Infineon Technologies has developed a RAG-Fusion chatbot to help engineers, account managers, and customers quickly access product information. This chatbot uses a combination of retrieval-augmented generation (RAG) and reciprocal rank fusion (RRF) to generate more accurate and comprehensive answers. RAG-Fusion creates multiple queries based on the original query, reranks them using RRF, and then fuses the results to provide a more relevant and detailed response. While RAG-Fusion provides accurate and comprehensive answers, it can sometimes stray off-topic when the generated queries are not relevant to the original question. The RAG-Fusion chatbot was tested for its ability to answer technical questions, sales-oriented questions, and customer-facing questions. It was found to be effective in answering technical questions related to products like MEMS microphones and MOSFETs. For sales-oriented questions, the chatbot provided strategies based on product knowledge and the latest standards, such as the IEEE 802.3bt standard for Power over Ethernet. For customer-facing questions, the chatbot provided information that helped customers understand the suitability of products for their needs. The chatbot also demonstrated the ability to provide information that was not explicitly mentioned in the product documentation, leveraging its pre-trained knowledge. However, it faced challenges such as slower response times due to the complexity of the RRF process and the need for more specific prompts to generate accurate answers. Additionally, the chatbot struggled to provide definitive negative answers when the necessary information was not available in the product database. Despite these challenges, RAG-Fusion was found to be more accurate and comprehensive than traditional RAG models. The chatbot's ability to generate multiple queries and rerank documents improved the quality of answers. Future improvements include expanding the chatbot to support multiple languages and enhancing the retrieval of information from multimodal documents. The chatbot also aims to integrate prompt engineering into its system to improve the accuracy of generated queries.Infineon Technologies has developed a RAG-Fusion chatbot to help engineers, account managers, and customers quickly access product information. This chatbot uses a combination of retrieval-augmented generation (RAG) and reciprocal rank fusion (RRF) to generate more accurate and comprehensive answers. RAG-Fusion creates multiple queries based on the original query, reranks them using RRF, and then fuses the results to provide a more relevant and detailed response. While RAG-Fusion provides accurate and comprehensive answers, it can sometimes stray off-topic when the generated queries are not relevant to the original question. The RAG-Fusion chatbot was tested for its ability to answer technical questions, sales-oriented questions, and customer-facing questions. It was found to be effective in answering technical questions related to products like MEMS microphones and MOSFETs. For sales-oriented questions, the chatbot provided strategies based on product knowledge and the latest standards, such as the IEEE 802.3bt standard for Power over Ethernet. For customer-facing questions, the chatbot provided information that helped customers understand the suitability of products for their needs. The chatbot also demonstrated the ability to provide information that was not explicitly mentioned in the product documentation, leveraging its pre-trained knowledge. However, it faced challenges such as slower response times due to the complexity of the RRF process and the need for more specific prompts to generate accurate answers. Additionally, the chatbot struggled to provide definitive negative answers when the necessary information was not available in the product database. Despite these challenges, RAG-Fusion was found to be more accurate and comprehensive than traditional RAG models. The chatbot's ability to generate multiple queries and rerank documents improved the quality of answers. Future improvements include expanding the chatbot to support multiple languages and enhancing the retrieval of information from multimodal documents. The chatbot also aims to integrate prompt engineering into its system to improve the accuracy of generated queries.