Tool-Specific Implementation AI Prompts

Apply Power BI best practices to build a robust, performant report. Report goal: {{goal}} Data model: {{data_model}} Power BI version / license: {{license}} 1. Data model best practices: - Star schema: always model as star schema — fact tables with foreign keys to dimension tables. Never use flat tables with denormalized data. - Relationships: one-to-many only in the model — many-to-many relationships cause performance problems. Resolve many-to-many with a bridge table. - Import vs DirectQuery: use Import mode for anything not requiring < 1-hour freshness. DirectQuery limits DAX features and is slow. - Composite model: use when some tables must be real-time (DirectQuery) and others can be imported. 2. DAX best practices: - Use variables (VAR ... RETURN): improves readability and performance by evaluating an expression only once - Avoid CALCULATE inside iterators (SUMX, COUNTX): nested iteration is slow - Time intelligence: always use a proper Date table marked as Date table in the model - Measures vs calculated columns: always prefer measures for aggregations. Calculated columns are stored in memory; measures compute on demand. 3. Report design: - Page tabs: meaningful names, not 'Page 1, Page 2' - Slicer placement: top or left of page, never inside the report body - Visual interactions: configure interactions explicitly — by default every visual cross-filters every other - Bookmarks: use bookmarks for show/hide panels, not for navigation (navigation pages are better) - Tooltips: create tooltip pages for rich hover details 4. Performance optimization: - Turn off 'Auto date/time': in Options > Data load. Auto date creates hidden date tables for every date column — major performance killer. - Reduce cardinality: avoid high-cardinality text columns in the fact table. Use integer keys and dimension tables. - Disable cross-highlighting on visuals not requiring it: each cross-highlight = a DAX query - Use Performance Analyzer (View > Performance Analyzer) to identify slow visuals 5. Governance: - Deployment pipelines: use Dev → Test → Production pipeline for enterprise reports - Row-level security (RLS): implement in the model, not in the report layer - Naming conventions: [Measure Name], d_DimensionTable, f_FactTable Return: data model validation checklist, DAX pattern examples for this use case, performance optimization steps, and naming conventions.

Write production-quality Python visualization code for this chart. Chart type: {{chart_type}} Data: {{data_description}} Library preference: {{library}} (matplotlib, seaborn, plotly, altair, bokeh) Output format: {{output}} (static PNG/PDF, interactive HTML, Jupyter notebook) 1. Library selection guide: Matplotlib: - Best for: publication-quality static charts, full control over every element - Pros: complete control, widely supported, PDF/SVG export - Cons: verbose API, interactive features require additional work Seaborn: - Best for: statistical charts (distributions, regressions, heatmaps) - Pros: high-level API, beautiful defaults, statistical integration - Cons: built on matplotlib, limited interactivity Plotly: - Best for: interactive web-based charts - Pros: interactive by default, Plotly Express high-level API, Dash integration - Cons: larger files, not ideal for publication Altair: - Best for: declarative grammar-of-graphics style charts - Pros: concise code, vega-altair grammar, responsive - Cons: data size limit in browser (5000 rows default) 2. Code standards: - Use matplotlib style sheets or seaborn themes for consistent styling - Set figure size explicitly: fig, ax = plt.subplots(figsize=(10, 6)) - Use clear variable names that match the data (not ax1, ax2) - Add docstring explaining what the function produces - Save with appropriate DPI: plt.savefig('chart.png', dpi=300, bbox_inches='tight') 3. Accessibility in code: - Set colorblind-safe palette: plt.rcParams['axes.prop_cycle'] = cycler(color=okabe_ito_palette) - Add alt text for web outputs - Ensure minimum font sizes: plt.rcParams['font.size'] = 12 4. Reusable function template: Write the chart as a function that accepts data and styling parameters: def create_[chart_name](data, title, color_col=None, highlight=None, save_path=None): 'Creates a [description] chart from the provided DataFrame.' ... return fig, ax 5. For this specific chart: - Import statements needed - Data preparation steps - Chart creation code with full styling - Annotation code - Save/display code Return: complete, runnable Python code with comments, color palette definition, and example usage.

Apply Tableau-specific best practices to build a high-quality, performant visualization. Visualization goal: {{goal}} Data source: {{data_source}} Tableau version: {{version}} 1. Data source best practices: - Extract vs live connection: use extract (.hyper) for anything not requiring real-time updates. Extract = 10–100× faster than live connections to most databases. - Data modeling in Tableau: use relationships (not joins) for multi-table data sources in Tableau 2020.2+. Relationships avoid row duplication from joins. - Calculated fields: compute in the data source (database/ETL) when possible. Tableau calculated fields that run on every row are slow. - LOD expressions: use for aggregations at a different level of detail than the viz. FIXED LOD does not respect dimension filters — use INCLUDE/EXCLUDE for filter-sensitive aggregations. 2. Performance best practices: - Limit mark count: < 5,000 marks for smooth interaction. > 50,000 marks = investigate alternatives (aggregation, sampling). - Filter order: context filters first → dimension filters → measure filters. Context filters run before the rest, dramatically reducing query scope. - Dashboard loading: disable 'Automatically update' for slow dashboards; provide a 'Run' button. - Hide unused fields: remove fields from the data source that are not used in the workbook. 3. Formatting standards: - Remove borders from all worksheets embedded in dashboards - Set background to 'None' on worksheets; control background at the dashboard layout level - Use Layout Containers (horizontal/vertical) to control spacing and alignment - Font: set a single font in Format > Workbook for consistency - Tooltip: customize all tooltips — default tooltips show field names (ugly) 4. Color in Tableau: - Use a custom color palette: Tableau's default palette is acceptable but not brand-aligned - For sequential palettes: use ColorBrewer palettes imported as custom palettes - Diverging palettes: always set the midpoint explicitly (not the data average unless that is meaningful) 5. Publishing and access: - Add descriptions to all worksheets and dashboards (used in Tableau Server search) - Tag dashboards by business domain for discoverability - Set refresh schedule to match data update frequency (not default 'never') 6. Calculated field documentation: - Add a comment to every complex calculated field explaining what it computes and why - Format: // Revenue excl. returns = gross revenue less refunds processed in the same period Return: implementation checklist, LOD expression examples for this use case, performance configuration, and formatting specification.