How to get Gemini to deeply understand your database

In the fast-evolving world of agentic development, natural language is becoming the standard for interaction. This shift is deeply connected to the power of operational databases, where a more accurate text-to-SQL capability is a major catalyst for building better, more capable agents. From empowering non-technical users to self-serve data, to accelerating analyst productivity, the ability to accurately translate natural language questions into SQL is a game-changer. As end-user engagements increasingly happen over chat, conversations become the fundamental connection between businesses and their customers. 

In an earlier post, "Getting AI to write good SQL: Text-to-SQL techniques explained," we explored the core challenges of text-to-SQL — handling complex business context, ambiguous user intent, and subtle SQL dialects — and the general techniques used to solve them.

Today, we're moving from theory to practice. We're excited to share that Google Cloud has scored a new state-of-the-art result on the BIRD benchmark's Single ...