BigQuery AI Operators
Independent PiSkill directory guide. The original prompt remains hosted by Google Cloud Generative AI.
What does this prompt do?
Demonstrates BigQuery AI operators for applying model capabilities to warehouse data with explicit SQL, input columns, result handling, and cost-aware validation.
Primary use case
Apply Gemini capabilities to data with BigQuery SQL.
Expected output
A reproducible BigQuery AI query and results.
Inputs or variables
- BigQuery table
- analysis instruction
- output fields
Related prompts
BigQuery RAG with Gemini
Builds a retrieval-augmented question-answering workflow with BigQuery data, embeddings, retrieval, prompt context, and grounded Gemini responses.
AI-Assisted Data Science
Uses Gemini to explore a data question, inspect a dataset, develop analysis steps, produce code, and explain findings with assumptions and validation checks.
Claude SQL Query Generation
Demonstrates a practical SQL-generation workflow that supplies schema context, asks for the correct dialect, constrains changes, and checks generated queries before execution.
Claude Text-to-SQL
Generates SQL from a natural-language question using explicit schema context, dialect constraints, validation, and handling for ambiguous or unsafe requests.
Evaluate LLM SQL Generation
Builds an evaluation workflow for generated SQL using representative cases, execution-aware checks, correctness criteria, and reproducible comparison rather than subjective inspection alone.
Gemini and Document AI Entity Extraction
Combines Document AI and Gemini to extract defined entities, reconcile OCR context, and assess whether structured results match the underlying document.