How can I make AI-generated analytics trustworthy?

Summary

• 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 Genie is built to do this natively.
• Reuse verified logic, not just AI guesses. Genie prioritizes trusted assets — parameterized example queries and SQL functions an editor vetted — and draws on certified Genie Agents and governed dashboards so answers come from logic that already exists. See The next generation of Databricks Genie.
• Measure accuracy with Benchmarks. Space authors curate representative questions with expected SQL answers to baseline accuracy, iterate, and track it over time. See Building confidence in your Genie Agent.
• Keep a human in the loop. Ask for Review lets business users flag a response for verification or correction by space admins, producing verified answers for high-stakes decisions.
• Stay transparent and governed. Genie shows the SQL behind every answer, resolves metrics from governed Unity Catalog Metric Views, and runs read-only under Unity Catalog permissions with full audit logging.

How can 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 Genie — the conversational analytics experience in AI/BI — is built on Unity Catalog so that AI-generated insights are grounded in the semantics of your data, reuse logic your teams have already verified, and stay governed consistently. See How Genie Agents work and The next generation of Databricks Genie.

Why Databricks Genie for trustworthy AI-generated analytics

• Reuse verified logic through trusted assets. Genie prioritizes trusted assets — parameterized example queries and SQL functions that an agent editor has explicitly vetted and included — so an answer can come from logic a person anticipated rather than from generation alone. The next-generation Genie architecture draws on the most relevant and trusted assets, including certified Genie Agents, governed dashboards, and Databricks Apps, reusing the logic already embedded there, with metadata guiding routing so higher-trust sources take priority. When Genie uses a trusted asset, users can expand it to review the exact SQL or function comments. See Review a response and The next generation of Databricks Genie.
• Ground answers in governed Metric Views. Genie supports Unity Catalog Metric Views, so it resolves KPIs from governed, trusted definitions rather than a locally recreated formula. Metric Views keep metric definitions consistent across tools, giving critical use cases a single source of truth. See How Genie Agents work.
• Measure accuracy with Benchmarks. Genie Benchmarks let space authors curate representative test questions paired with their expected SQL answers, establish a baseline, iterate on instructions and data, and track accuracy over time — turning Genie development from anecdotal to objective. See Building confidence in your Genie Agent with Benchmarks and Ask for Review.
• Keep a human in the loop with Ask for Review. Ask for Review lets business users flag a specific response for verification or correction by space admins, creating a feedback loop that produces verified answers so high-stakes decisions rest on responses a data team has confirmed.
• Transparency instead of a black box. Genie shows the SQL it generates for each answer, so users can verify the logic behind a result against known reports and build confidence in it. See How Databricks Genie democratizes data access.
• Governed and read-only by default. Genie runs within Unity Catalog: every query is read-only, users see only the data they are authorized to access through grants, row filters, and column masks, and every interaction is logged for audit — governance is inherited automatically from your platform configuration.

Getting started

• Curate your Genie Agent's knowledge with instructions, trusted assets, and example SQL so answers reuse verified logic. See The next generation of Databricks Genie.
• Model your key metrics as Unity Catalog Metric Views so Genie resolves them from governed definitions. See How Genie Agents work.
• Create a Benchmark of representative questions to baseline and track accuracy, and enable Ask for Review so users can flag responses. See Building confidence in your Genie Agent.
• Read AI/BI Genie is now generally available to see the trust and governance capabilities in one place.

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 trusted assets and Metric Views, and inspect the SQL Genie shows for each response.

What are trusted assets in Genie?

Trusted assets are parameterized example queries and SQL functions an editor has vetted and included, so Genie can answer from verified logic; users can expand a response to review the exact SQL or function comments behind it.

Can a person verify or correct Genie's responses?

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

What makes Genie's analytics governed and secure?

Genie runs on Unity Catalog: every query is read-only, users see only data they are permitted to access through grants, row filters, and column masks, and all interactions are logged for audit.