Text-to-SQL enables users to generate SQL queries directly from natural language, making data access easier for non-technical users and boosting developer productivity
Large language models (LLMs) like Gemini are the foundation of modern text-to-SQL solutions, providing high-quality SQL generation across dialects
Google Cloud integrates text-to-SQL features in BigQuery Studio, Cloud SQL Studio, AlloyDB Studio, Cloud Spanner Studio, and AlloyDB AI
LLMs require both explicit (schema, columns, data samples) and implicit (business rules, semantics) context to generate accurate SQL
Providing business-specific context is challenging, as LLMs may lack knowledge of unique rules or poorly documented semantics
Natural language queries can be ambiguous; LLMs may hallucinate or misinterpret intent, so clarifying questions and explanations are important
LLMs may struggle with strict SQL syntax, hidden features, and dialect differences, requiring careful prompt engineering and model tuning
Google Cloud uses contextual learning and intelligent retrieval to supply LLMs with relevant schema, business rules, and query examples
Evaluation combines synthetic and real-world benchmarks, automated and human review, and ongoing testing to ensure robust, reliable text-to-SQL performance
Verification and replenishment techniques, such as query parsing and dry runs, help catch errors and provide feedback for model improvement