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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale
- Enterprise data, spanning diverse domains, often maintained across disparate environments, poses challenges for natural language to SQL (NL2SQL) technology due to complex schemas with nested tables and multi-dimensional data.
- Recent advances in generative AI have enabled NL2SQL technology using large language models (LLMs), but accuracy and scalability remain challenges for enterprise data.
- Challenges include complex schemas optimized for storage, diverse and complex natural language queries, LLM knowledge gap, attention burden, and fine-tuning challenges.
- A solution methodology has been developed by AWS and Cisco teams that focuses on narrowing the generative focus to the appropriate data domain, using data abstractions, and optimizing SQL generation steps.
- The methodology involves mapping user queries to data domains, scoping data domains for prompt construction, augmenting SQL DDL definitions, determining query dialect, and managing identifiers for SQL generation.
- Handling complex data structures involves abstracting domain data structures into simplified forms for better understanding by the LLMs.
- The solution provided high accuracy, consistency, low cost, low latency, and scalability in SQL generation for enterprise data, achieved through the systematic approach outlined in the methodology.
- The solution's architecture on AWS involves processing steps using Amazon API Gateway, AWS Lambda, and Amazon Bedrock to process natural language queries into SQL results.
- In conclusion, the methodology offers a methodical approach to enterprise-grade SQL generation, reducing complexity, ensuring accuracy, and improving overall performance.
- Authors include professionals from Cisco and AWS with extensive experience in AI/ML, cloud migration, computer science, engineering, and security domains.
- The solution methodology can be adapted to various business applications, with a demo code available in the GitHub repository, inviting feedback and questions.
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