Getting AI to write good SQL (cloud.google.com)

Article summary

The article discusses the challenges of using AI to generate SQL queries from natural language input, including providing business-specific context, understanding user intent, and managing differences in SQL dialects. Google's Gemini model is presented as a solution, which uses techniques such as intelligent retrieval, in-context learning, and validation to improve the quality of generated SQL queries. The article also highlights the importance of a semantic layer in providing context for the AI model. The use of Gemini is available in various Google Cloud products, including BigQuery Studio and Cloud SQL Studio.

Main themes

• AI-generated SQL queries
• semantic layer
• SQL dialects
• natural language input
• query precision
• JSON vs SQL
• database constraints
• AI reliability

What commenters say

• Using a semantic layer is the best way to get better precision from AI-generated SQL queries.
• Expressing queries in JSON instead of raw SQL is not necessarily an improvement.
• The use of AI to generate SQL queries is already solved with current technology.
• The quality of the input schema and constraints is crucial for AI-generated SQL queries to be accurate.
• AI-generated SQL queries can be unreliable and may require human validation.
• The use of a semantic layer can make it easier for AI models to understand the context of the query.
• The conversion of JSON to SQL can be complex and may not always produce the desired results.
• The main challenge in using AI to generate SQL queries is understanding the user's intent and providing the correct context.