How do I make AI-generated analytics trustworthy?

Summary

• Ground answers in your governed semantics. With Databricks AI/BI, Genie Agents built on Unity Catalog Metric Views resolve business metrics from deterministic definitions, so answers come from a single source of truth rather than inferred logic. See Redefining the semantics data layer.
• Use certified answers and trusted assets. Certified answers let Genie invoke governed logic from Unity Catalog Functions and Metrics as tools, and instructions plus example SQL teach it your organization's language. See Introducing AI/BI.
• Measure accuracy with Benchmarks. Space authors curate representative questions with expected SQL answers to establish a baseline, iterate on improvements, and track accuracy over time. See Building confidence in your Genie Agent.
• Keep a human in the loop. Request Review lets business users flag responses for verification by data teams, creating verified answers for high-stakes decisions.
• Stay transparent and governed. Genie shows the SQL it generates so results can be verified, and every query runs read-only under Unity Catalog permissions with full audit logging.

How do I make AI-generated analytics trustworthy?

Trustworthy AI-generated analytics come from grounding the AI in governed data and definitions, measuring its accuracy, keeping people in the loop, and making every answer transparent and auditable. Databricks AI/BI — the Genie conversational experience and AI/BI dashboards — is built on Unity Catalog so that AI-generated insights are grounded in the semantics of your data and governed consistently. See What is AI/BI? and the Databricks business intelligence product page.

Why Databricks AI/BI for trustworthy AI-generated analytics

• Grounded in your business semantics. Genie Agents created on Unity Catalog Metric Views are grounded in governed, deterministic definitions from your semantic layer. Because Metric Views compile into logical queries at runtime, users receive correct, consistent results — Genie resolves metrics from a single source of truth rather than inferring them. See Redefining the semantics data layer for the future of BI and AI.
• Certified answers and trusted assets. Certified answers let Genie invoke governed logic packaged as Unity Catalog Functions and Metrics as "tools," so answers derive from deterministic, governed definitions. When Genie uses these certified assets it surfaces that trusted status to the user. The Genie knowledge store also holds instructions that teach Genie your organizational logic and language, plus example SQL and metadata such as column synonyms, example values, and join relationships — all without modifying upstream Unity Catalog assets. See Introducing AI/BI: Intelligent Analytics for Real-World Data.
• Benchmarks to measure and improve accuracy. Genie Benchmarks let space authors create curated test questions with expected SQL answers, so you can evaluate accuracy systematically. Teams typically define a set of representative questions chosen by subject-matter experts and end users, establish a baseline, then iteratively apply improvements and measure their impact — turning Genie development from anecdotal to objective. See Building confidence in your Genie Agent with Benchmarks and Ask for Review and How to build production-ready Genie Agents.
• Human review with Request Review. Request Review ("Ask for Review") lets end users flag a specific Genie response for verification or correction by space admins. This creates a feedback loop where users can request confirmation of critical responses and receive verified answers, so high-stakes decisions rest on responses a data team has confirmed.
• Transparency instead of a black box. Genie is designed to be inspectable: users see the SQL queries it generates for each answer, which lets them verify the logic behind a result and build confidence in it.
• Governed by Unity Catalog. AI/BI operates within Unity Catalog access policies. Users see only the data they are authorized to access, every Genie query is read-only, and all interactions are logged for audit. Governance policies, lineage, and security controls are applied consistently, so the analytics experience stays governed and compliant. See What is AI/BI?.

Getting started

• Model your key metrics as Unity Catalog Metric Views so Genie and dashboards resolve them from governed definitions. See Redefining the semantics data layer.
• Curate your Genie Agent's knowledge store with instructions, certified answers, and example SQL, following the best-practices guide for building high-performing Genie Agents.
• Create a Benchmark of representative questions to baseline and track accuracy, and enable Request Review so users can flag responses for verification. See Building confidence in your Genie Agent.
• Govern access and audit usage through Unity Catalog. Review What is AI/BI? to understand how governance is applied.

FAQs

How do I know an AI-generated answer is accurate?

Use Genie Benchmarks to test the space against curated questions with expected SQL answers and track accuracy over time, ground answers in certified assets and Metric Views, and inspect the generated SQL Genie shows for each response.

What makes AI/BI answers governed and secure?

AI/BI runs on Unity Catalog: users see only data they are permitted to access, every Genie query is read-only, and all interactions are logged for audit, with lineage and security controls applied consistently.

Can a person verify or correct Genie's responses?

Yes. Request Review lets business users flag a specific response for verification or correction by space admins, producing verified answers for high-stakes decisions.

What are certified answers in Genie?

Certified answers let Genie invoke governed logic packaged as Unity Catalog Functions and Metrics as tools, so answers derive from deterministic, governed definitions and Genie shares that trusted status with users.