ChatGPT Prompts for Power BI (DAX, Dashboards, Modeling)
20 copy-paste ChatGPT prompts for Power BI: DAX formulas explained, dashboard design, data modeling, M/Power Query, and the report-writing that turns dashboards into decisions.
DAX Formulas
4 promptsDAX Formula Generation
1/20I need DAX formula for [calculation]. Context: [tables, columns, relationships]. Output: DAX expression, explanation of each function, common mistakes, performance considerations (CALCULATE filters, iterators). Verify in Power BI before deploy.
Generates DAX formulas.
Pro tip: DAX is its own language. AI helps draft + explain; test in Power BI before deploy. Hallucinated DAX possible; verification mandatory.
DAX Optimization
2/20[Paste slow DAX]. Optimize for performance. Output: rewritten formula, why slow (iterators, filter context, table scans), expected improvement, alternative approaches. Performance matters at scale.
Optimizes DAX performance.
Pro tip: Slow DAX = unusable dashboard. Common culprits: nested CALCULATE, row-level iteration, unrelated filter context. Targeted optimization = 10-100x speedup.
Time Intelligence DAX
3/20DAX for time intelligence: [YTD / MTD / vs prior period / rolling 12]. Output: formula using DAX time-intelligence functions (SAMEPERIODLASTYEAR / DATEADD / DATESYTD), date table requirement, common pitfalls. Date table essential.
Writes time intelligence DAX.
Pro tip: Time intelligence requires marked date table. Without it, time functions return wrong results. Always check: model has date table + marked + relationships.
DAX Debugging
4/20[Paste DAX returning wrong result]. Debug: what I expected, what it returns, where mismatch likely. Walk through filter context step-by-step. Suggest test variants. DAX debugging is filter-context puzzle.
Debugs DAX errors.
Pro tip: DAX returning unexpected = filter context wrong. Walk filter context step-by-step. CALCULATE modifiers, REMOVEFILTERS, ALL — each changes context. Mental model first.
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Data Modeling
4 promptsStar Schema Design
5/20Design star schema for [domain — sales / HR / finance]. Output: fact table, dimension tables, relationships, granularity per fact, slowly-changing dimensions handling. Star schema > flat tables for performance + clarity.
Designs star schemas.
Pro tip: Power BI optimized for star schema. Flat tables = poor performance + confusing measures. Star schema upfront = sustainable model.
Relationship Cardinality
6/20Help me decide relationship cardinality between [tables]. Many-to-one (recommended), one-to-one (rare), many-to-many (use carefully). Output: recommended cardinality, why, cross-filter direction, performance implications.
Plans table relationships.
Pro tip: Many-to-many relationships = often modeling smell. Either dimension table missing or shared dimension needed. Avoid m:m where possible; design dimension out.
Power Query / M Language
7/20Power Query M for [transformation needed]. Output: M code, step-by-step explanation, performance considerations (query folding), error handling. M is unfamiliar to many; explain like first time.
Writes Power Query M code.
Pro tip: M language poorly documented. AI helps draft + explain. Test in Power Query editor; verify query folds for performance (server pushdown).
Data Refresh Strategy
8/20Refresh strategy for [dataset]. Output: scheduled refresh frequency, incremental refresh setup, gateway requirements, monitoring + alerts on failure, capacity planning. Refresh failures = stale dashboards = lost trust.
Plans data refresh strategies.
Pro tip: Daily refresh fails silently for weeks = stale dashboards. Alerting on failure + monitoring SLA = users trust dashboards. Without monitoring = invisible decay.
Dashboard + Report Design
4 promptsDashboard Layout
9/20Layout for [dashboard purpose]. Audience: [executive / operational / analytical]. Output: top-of-page key metrics (5-7 KPIs), filter pane, chart hierarchy by importance, drill-down navigation, mobile considerations. Design for the audience's decision.
Designs dashboard layouts.
Pro tip: Executive dashboard = 5-7 KPIs scannable in 30 sec. Operational = real-time + alerting. Analytical = drill-deep capability. Audience drives design.
Visual Chart Selection
10/20For [data type + question], best Power BI visual? Options: bar, line, scatter, treemap, KPI card, gauge, table, custom visual. Match visual to question, not data structure. "Show trend" = line. "Compare 5 things" = bar. "Show 100 things" = table.
Selects appropriate visuals.
Pro tip: Pie charts for >3 categories = unreadable. Gauges for KPIs not pacing toward target = confusing. Bar > pie for compare. Match visual to question.
KPI Card Design
11/20KPI card for [metric]. Output: current value, comparison (vs target / vs prior period), trend indicator (sparkline or arrow), color logic (green/yellow/red thresholds), drill-through. KPI cards prime real estate; design intentionally.
Designs KPI cards.
Pro tip: KPI without comparison = number floating. KPI with target + trend = decision-ready. Comparison context is what makes a number a KPI.
Color + Accessibility
12/20Power BI report color audit. Check: brand alignment, accessibility (color-blind friendly palettes), red/green for non-critical (avoid for color-blind), label visibility, contrast ratios. Reports used by everyone need accessibility.
Audits Power BI accessibility.
Pro tip: Red/green encoding = invisible to 8% of men (color-blind). Blue/orange or shape encoding = accessible. Most BI defaults fail accessibility; conscious choice required.
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Reports + Insights
4 promptsInsight Narrative from Dashboard
13/20[Paste dashboard data summary]. Write insight narrative for [audience]: 3 key findings, what changed + why hypothesis, recommended actions, what to watch next period. Dashboards show; narratives explain.
Writes insight narratives.
Pro tip: Dashboards alone = "look at this." Narratives + dashboards = "do this because of this." Decision-driven beats data-driven.
Variance Explanation
14/20Top variance: [metric] changed [X%] vs [period]. Help me explain: candidate causes (decompose by dimension), data to verify, narrative for stakeholders, action implications. Variance > 5% = explain proactively.
Explains variances.
Pro tip: Stakeholders see variance > 5% in meeting = "why?" question. Explained proactively = analyst credibility. Surprised by question = looks unprepared.
Executive Summary Slide
15/20Convert [dashboard] to 1-slide executive summary. Output: top KPIs (3-5), key trend, biggest concern, biggest opportunity, recommended action. Executive can't click into Power BI; static slide for them.
Converts dashboards to exec slides.
Pro tip: Power BI dashboards interactive. Executives often want static slide. Distill dashboard to slide = reach audience that won't click.
Anomaly Detection Setup
16/20Anomaly detection in Power BI for [metric]. Output: enable in visual, sensitivity setting, expected output, what to do with detected anomalies (alert, investigate). Power BI has built-in; underused.
Sets up Power BI anomaly detection.
Pro tip: Power BI built-in anomaly detection (AI-powered) = visible spikes/dips automatically. Most users don't enable; data tells them what changed.
Frequently Asked Questions
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