Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

This article discusses an advanced enterprise-grade approach to natural language to SQL (NL2SQL) generation using large language models (LLMs), focusing on balancing accuracy, latency, and scalability.

• Challenges include complex database schemas, diverse query types, and LLM knowledge gaps
• Solution involves breaking down NL2SQL generation into focused, sequential steps
• Key optimization techniques include:
  • Mapping queries to specific data domains
  • Resolving identifiers before SQL generation
  • Abstracting complex data structures
  • Augmenting data with detailed definitions
• Tested solution showed over 95% accuracy and consistency
• Enables use of smaller, more cost-effective LLMs

The methodology provides a scalable approach to generating accurate SQL queries from natural language across complex enterprise data environments.