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advanced·Data & Analytics·1.5 hours

Prepare Your Power BI Semantic Model for AI & Copilot

Eight field descriptions, complete AI Instructions text, synonym list, and a gap analysis flagging which natural-language questions your model can and cannot yet answer.

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Recommended modelClaude Sonnet 4.6

Deep BI workflow design and data pipeline reasoning

What you need to fill in

[list key tables][list 5–10 important measures with brief descriptions][describe main relationships][primary audience][fiscal calendar — standard or custom start month]

Tools used

Claude SonnetPower BI DesktopPower BI Service

The prompt

"I want to optimise my Power BI semantic model so that Copilot and AI Q&A give accurate, reliable answers. Model overview: - Key tables: [list] - Most important measures: [list 5–10 with a brief description of each] - Main relationships: [describe, e.g. Sales[CustomerID] → Customer[ID], many-to-one] - Primary audience: [e.g. Finance team, regional sales managers] - Fiscal calendar: [standard Jan–Dec / custom — specify start month] Please produce: 1. Field-level descriptions for the 8 most Copilot-critical columns (plain English, ≤ 2 sentences each) 2. The full AI Instructions text for the 'Prepare data for AI' panel in Power BI Service 3. A synonym list for the top 5 measures (natural-language aliases your audience would use) 4. A gap analysis: list the 5 most likely natural-language questions your audience will ask, and flag which ones your current model cannot answer and why"
Open in Claude

Sample output

**Field descriptions (sample):** - `Net Revenue`: Total sales after discounts and returns, in USD. Excludes intercompany and contra-revenue items. - `Gross Margin %`: Gross profit divided by Net Revenue. Use this — not Revenue — to compare profitability across segments. - `Close Date`: Date a deal was marked Won in Salesforce. Use for period reporting; distinct from Invoice Date. - `ARR`: Annual Recurring Revenue. Annualised value of active subscriptions; excludes one-time fees. **AI Instructions text:** > "This model covers B2B Sales performance for the Americas region. 'Revenue' always means Net Revenue after returns. For 'targets', use the [Quota] table, not [Forecast]. Our fiscal year starts February 1 — when asked about 'this year' or 'YTD', use fiscal year. Default to current fiscal period unless the user specifies otherwise. 'Churn' refers to the [Churned ARR] measure." **Synonyms:** - Net Revenue → Sales, Bookings, Income, Top Line - Gross Margin % → Margin, Profitability, GM% - Churned ARR → Churn, Lost Revenue, Cancellations **Gap analysis:** - ✅ 'What is our revenue this quarter?' — answerable - ✅ 'Which region is furthest behind target?' — answerable - ❌ 'Why did churn spike in March?' — needs a narrative/commentary measure; model has no causal layer - ❌ 'What will Q4 look like?' — no forecast table connected; add [Forecast] table - ⚠️ 'Show me deals at risk' — partial: pipeline data exists but no risk-score measure

Try with these tools

claudechatgptnotebooklm
power-bisemantic-modelcopilotai-prep

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