Generating value from enterprise data: Best practices for Text2SQL and generative AI

This article discusses how Text2SQL (natural language to SQL) can be used to generate value from enterprise data by enabling non-technical users to query databases using natural language. It provides an overview of Text2SQL and explores design patterns, best practices, and architectures for implementing such systems.

Specifically, the article covers:

• Why Text2SQL is needed to make database queries more accessible to non-technical users
• Key components of Text2SQL systems: natural language processing, SQL generation, and database querying
• Prompt engineering considerations for using large language models to translate natural language to SQL
• Optimization techniques like caching, monitoring, materialized views, and using a central data catalog
• Architecture patterns: prompt engineering, fine-tuning, and retrieval augmented generation (RAG)
• Conclusion on using Amazon Bedrock for building and scaling generative AI applications