Guide 01
Failure diagnostic matrixWhy Power BI Copilot gives wrong answers
Trace plausible-but-wrong Power BI Copilot answers to ambiguity in the semantic model, AI configuration, security context, or evaluation process.
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Power BI Copilot readiness, without the hand-waving.
Six practical guides for tracing wrong answers, preparing semantic models, choosing the right product surface, and testing what comes back. Microsoft product facts are separated from Refinity recommendations.
Guide 02
21-point evidence checklistPower BI Copilot readiness checklist
A 21-point, evidence-based checklist for deciding whether a Power BI semantic model is ready for responsible Copilot testing.
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Five-pass build-and-test workflowHow to prepare a Power BI semantic model for AI
A build-and-test workflow for turning a dashboard-ready Power BI model into a defensible foundation for natural-language analytics.
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Control-selection matrixAI data schemas vs. AI instructions vs. verified answers
Choose the right Power BI Prep data for AI control for field ambiguity, business context, and high-value known answers.
Read the guideGuide 05
Architecture decision guidePower BI Copilot vs. Fabric Data Agent vs. custom agent
A decision guide for choosing an embedded Power BI experience, a multi-source Fabric data agent, or a Copilot Studio custom agent.
Read the guideGuide 06
Eight-part evaluation rubricHow to evaluate natural-language analytics answers
Build golden questions, expected-answer contracts, role-based tests, and release gates for Copilot and other natural-language analytics experiences.
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Score one model across all seven dimensions.
The scorecard is ungated, takes about 8–12 minutes, and keeps answers in your browser tab.