SQL reimagined for the AI era with BigQuery AI functions

Built-in optimizations

These functions allow you to easily mix AI processing with common SQL operators like WHERE, JOIN, ORDER BY, and GROUP BY. BigQuery handles prompt optimization, model selection, and model parameter tuning for you. 

• Prompt optimization: LLMs are sensitive to the wording of a prompt, the same question can be expressed in different ways which affect quality and consistency. BigQuery optimizes your prompts into a structured format specifically for Gemini, helping to ensure higher-quality results and an improved cache hit rate.

• Query plan optimization: Running generative AI models over millions of rows can be slow and expensive. BigQuery query planner reorders AI functions in your filters and pulls AI functions out from join to reduce the number of calls to the model, which saves costs and improves performance.

• Model endpoint and parameter tuning: BigQuery tunes model endpoint and model parameters to improve both result quality and results consistency across query runs.

Get started

The new managed AI functions — AI.IF() , AI.SCORE() and AI.CLASSIFY() — complement the existing general-purpose Gemini inference functions such as AI.GENERATE from BigQuery. In addition to optimizations discussed above, you can expect future optimization and mixed query processing between BigQuery and Gemini for even better price-performance. You can indicate your interest for early access here. 

What to use and when: When your use case fits them, start with the managed AI functions as they are optimized for cost and quality. Use the AI.GENERATE family of functions when you need control on your prompt and input parameters, and want to choose from a wide range of supported models for LLM inference.

To learn more refer to our documentation. The new managed AI functions are also available in BigQuery DataFrames. See this notebook and documentation for Python examples.

For questions or feedback, reach out to us at bqml-feedback@google.com.