Note: The topics referenced throughout this document refer to the new Topics experience (not legacy Topics). For details on the differences, see Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick.

Most real-world business questions span multiple tables. A retailer who wants to understand net revenue by product category must draw from a sales fact table, a returns fact table, and a product dimension. Each of these lives in a separate dataset. Until recently, bridging those datasets required a data engineer to pre-join them and deliver a single dataset to Amazon Quick Sight before any analyst could ask a question.

Amazon Quick Sight’s Multi-Dataset Topics change that equation by letting analytics teams bring multiple datasets into a single Topic in one of two ways. You can define explicit relationship keys (covered in the companion post, Data modeling best practices for Amazon Quick Sight multi-dataset relationships , or you can equip the generative AI engine with enough semantic context to write SQL itself. This post focuses on the second path: Chat-powered, AI-generated SQL.

When you configure a Topic for Chat, you do not need to define relationships in advance. Instead, you author a semantic layer that includes dataset-level custom instructions, topic-level instructions, field synonyms, and field descriptions. The AI uses that context to generate context-aware SQL at query time. This puts outer joins, unions, subqueries, self-joins, cross-grain comparisons, and conditional join logic all within reach, with no structural constraint on the relationship graph.