By name: aibi-dashboard-guardrails description: Prevent repeated mistakes when building or updating Databricks AI/BI dashboards from semantic YAML files, schema definitions, and user requirements. Use when working on dashboard KPIs, display tables, formatting rules, prompt-vs-YAML conflicts, or cross-checking user-supplied expected values.
AI/BI Dashboard Guardrails
Apply these rules before changing any dashboard.
Core rules
- Treat the user's prompt as the highest-priority source of truth.
- Treat schema and semantic YAML files as supporting context, not override authority.
- Validate every KPI formula with explicit SQL before publishing.
- If the user gives expected values, cross-check the dashboard against those values before publishing.
- Separate raw metric datasets from display-shaped reporting datasets when the user expects rounded, formatted, or decorated table values.
Read these references as needed
- For prompt vs YAML conflicts: references/01-prompt-overrides-yaml.md
- For ambiguous measure names: references/02-measure-name-ambiguity.md
- For display tables vs raw datasets: references/03-display-vs-raw-datasets.md
- For unit and formatting mistakes: references/04-formatting-and-units.md
- For user-provided cross-check tables: references/05-cross-check-procedure.md
- For pre-publish workflow: references/06-prepublish-checklist.md
Mandatory behavior
- State any prompt-vs-YAML conflict explicitly before implementing.
- If a measure name is ambiguous, clarify whether it is gross, net, filtered, cumulative, or display-only.
- Do not assume compact currency formatting is acceptable. Check the requested presentation first.
- Use display-shaped datasets only when the user expects presentation-ready table values rather than raw numeric fields.
Output discipline
- When correcting a dashboard, say whether the fix is formula-only, formatting-only, dataset-only, or validation-related.
- Publish only after SQL validation and final business-value cross-checks are complete.