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

This blog post is co-written with Renuka Kumar and Thomas Matthew from Cisco.

Enterprise data by its very nature spans diverse data domains, such as security, finance, product, and HR. Data across these domains is often maintained across disparate data environments (such as Amazon Aurora, Oracle, and Teradata), with each managing hundreds or perhaps thousands of tables to represent and persist business data. These tables house complex domain-specific schemas, with instances of nested tables and multi-dimensional data that require complex database queries and domain-specific knowledge for data retrieval.

Recent advances in generative AI have led to the rapid evolution of natural language to SQL (NL2SQL) technology, which uses pre-trained large language models (LLMs) and natural language to generate database queries in the moment. Although this technology promises simplicity and ease of use for data access, converting natural language queries to complex database queries with accuracy and at enterprise scale has ...