Choosing the right approach for generative AI-powered structured data retrieval

Organizations want direct answers to their business questions without the complexity of writing SQL queries or navigating through business intelligence (BI) dashboards to extract data from structured data stores. Examples of structured data include tables, databases, and data warehouses that conform to a predefined schema. Large language model (LLM)-powered natural language query systems transform how we interact with data, so you can ask questions like “Which region has the highest revenue?” and receive immediate, insightful responses. Implementing these capabilities requires careful consideration of your specific needs—whether you need to integrate knowledge from other systems (for example, unstructured sources like documents), serve internal or external users, handle the analytical complexity of questions, or customize responses for business appropriateness, among other factors.

In this post, we discuss LLM-powered structured data query patterns in AWS. We provide a decision framework to help you select the best pattern for your specific use ...