How do I prevent AI from giving inconsistent answers about patient outcomes or financial metrics?

Summary

• AI gives inconsistent answers when a metric like a patient-outcome rate or a financial KPI is defined several different ways across dashboards, notebooks, and people's heads — the AI faithfully reproduces the ambiguity. The fix is to define each metric once as a governed object.
• On Databricks, Metric Views let you define a business metric a single time as a versioned Unity Catalog object, so every query — from AI/BI Genie, a dashboard, or another tool — resolves the same approved calculation.
• Unity Catalog business semantics add a shared semantic layer — synonyms, descriptions, display rules, and example queries — that serves both human analysts and AI from one governed source.
• AI/BI Genie is integrated with these semantics: Genie Agents built on Metric Views resolve metrics from a single source of truth rather than inferring logic, so natural-language answers are consistent.
• Metric Views inherit Unity Catalog governance — permissions, lineage, audit, and row/column security — so a sensitive metric (like a clinical or financial measure) can be exposed as a governed aggregate without granting broad table access.

How to prevent AI from giving inconsistent answers about patient outcomes or financial metrics

Inconsistent AI answers usually are not an AI problem — they are a definition problem. When a metric such as a readmission rate, an availability percentage, or a revenue figure is calculated several different ways across dashboards, notebooks, and spreadsheets, an AI assistant faithfully reproduces the ambiguity and different questions return different numbers. The durable fix is to define each metric once, govern it, and make every consumer — human or AI — resolve that same definition. On Databricks this is done with Metric Views and Unity Catalog business semantics, with AI/BI Genie grounded on top of them.

Why Databricks keeps AI answers on metrics consistent

• Define each metric once. Metric Views let you declare a business metric a single time as a versioned Unity Catalog object — a source table, optional joins, dimensions to group by, and measures with their calculation formulas — then query it at any grouping or filter without re-deriving the aggregation. The business logic is encoded once, so a patient-outcome rate or a financial measure is computed the same way everywhere.
• One definition, every consumer. Because the metric formula lives in the metric view rather than in individual reports, every query from Genie, a dashboard, or another BI tool relies on the same approved definition. When the definition changes, you update it in one place and every consumer inherits the change — no more competing numbers for the same metric.
• A shared semantic layer. Unity Catalog business semantics enrich metrics with natural-language synonyms, descriptions, display rules (for example, format as currency or round to two decimals), example queries, and domain-specific instructions. This layer is designed as a shared service that serves both human analysts and automated systems from a single governed source, rather than logic that lives only inside one dashboard.
• Genie resolves, not guesses. AI/BI Genie is integrated with Unity Catalog business semantics, and Genie Agents can be created directly on Metric Views. Every natural-language query is grounded in governed, deterministic definitions instead of inferred logic — the metric views compile into logical queries at runtime — so Genie resolves a metric from a single source of truth rather than reconstructing it.
• Governed and secure by inheritance. Metric Views inherit Unity Catalog's governance model: permissions, lineage, and audit logs. Row- and column-level security applies, attribute-based access control propagates to derived metrics, and definer security lets a metric view expose a governed aggregate without granting broad underlying-table access to every consumer — important for sensitive clinical or financial data.
• Consistent dashboards too. AI/BI Dashboards powered by governed metrics ensure every visual, filter, drill-through, and comparison uses the same certified measures and dimensions, so numbers stay consistent across teams and tools.

Getting started

• Read redefining the semantics data layer for BI and AI for how business semantics power consistent AI/BI answers.
• Learn data modeling in AI/BI dashboards and start with 3-5 key metrics that solve immediate pain points, then expand.
• Use local metric views for analysis-specific logic that is not yet hardened, and promote them to Unity Catalog when they are ready for broader use.
• See why agentic analytics starts with a well-governed data layer and semantic layer architecture for AI.

FAQs

Why does AI give different numbers for the same metric?

Usually because the metric is defined multiple ways across dashboards, notebooks, and spreadsheets. The AI reproduces whichever logic it finds. Defining the metric once as a governed Metric View gives every consumer the same calculation.

How does a Metric View keep answers consistent?

A Metric View encodes a metric's source, joins, dimensions, and measure formulas once as a versioned Unity Catalog object. Genie, dashboards, and other tools all resolve that single definition, and updating it in one place propagates to every consumer.

Does this stop Genie from making up metrics?

Genie is integrated with Unity Catalog business semantics and can run on Metric Views, so it resolves metrics from governed, deterministic definitions compiled into logical queries at runtime rather than inferring the logic — which keeps its natural-language answers consistent.

Can I expose a sensitive metric without exposing the raw data?

Yes. Metric Views inherit Unity Catalog governance, including row/column security and definer security, so a metric view can return a governed aggregate — such as a clinical or financial measure — without granting broad access to the underlying tables.