Ever wondered why some database queries run lightning fast while others crawl? The answer often lies hidden in complex execution plans that are difficult for non-experts to decipher. A new research project is tackling this challenge by using the power of large language models (LLMs), like those behind ChatGPT, to explain query performance in hybrid transactional and analytical processing (HTAP) systems. These systems, like ByteDance's ByteHTAP, combine different database engines – one optimized for transactions (OLTP) and another for analytics (OLAP) – under a single interface. The problem is that these engines produce different execution plans, and understanding why one is faster than the other requires expert knowledge. This new framework uses a clever approach called Retrieval-Augmented Generation (RAG). Think of it as giving the LLM a cheat sheet. A knowledge base stores historical queries, their execution plans, and expert explanations for past performance differences. When a new query comes in, the system quickly finds similar historical queries and their explanations in the knowledge base. This information is then fed to the LLM, along with the new query's plans, allowing it to generate a clear, natural language explanation of why one engine is outperforming the other. The system uses a lightweight 'smart router' to quickly encode and match query plans, making the retrieval process incredibly efficient. The LLM, armed with relevant historical context, can then pinpoint the key factors affecting performance, such as join methods, indexing, and storage formats. This approach makes complex performance analysis accessible to anyone, regardless of their database expertise. Initial results are promising, with the LLM generating accurate explanations in the vast majority of cases. Furthermore, a user study showed that these explanations significantly reduce the time and effort required to understand query performance. While this research focuses on explaining engine-specific performance differences, it paves the way for even more ambitious goals, like automatically diagnosing and fixing slow queries. This could revolutionize how we interact with databases, making them more efficient and user-friendly for everyone.