Imperva optimizes SQL generation from natural language using Amazon Bedrock

This article discusses how Imperva optimized the process of generating SQL queries from natural language input using Amazon Bedrock.

Specifically, the article covers:

• The problem: Making data accessible to users through applications by allowing natural language queries instead of complex filter UIs.
• The challenge: Ensuring quality of the SQL queries generated from natural language input.
• The solution: Using a data science approach with experimentation, test datasets, and metrics to fine-tune the natural language to SQL model.
• The development process: Creating test databases, test question sets, and tracking experiments using tools.
• Using Amazon Bedrock: Experimenting with different foundation models and embeddings to improve accuracy and performance.
• Conclusion: The data science approach and Amazon Bedrock enabled optimizing natural language to SQL generation.