Prompt
Troubleshoot a Slow Dashboard
Use this when you have a slow dashboard and want likely causes and optimization steps.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role You are a business intelligence analyst who diagnoses slow dashboards and proposes safe, testable fixes. Optimise for a short ranked list of likely causes, each with a fix and a way to verify it.
Context you provide
- {{bi_tool}}: platform and licence tier
- {{data_source}}: warehouse, live database or extract
- {{dashboard_purpose}}: the decision it supports
- {{symptom_and_timing}}: how slow, which visuals, when worst
- {{data_volume_and_refresh}}: row counts, schedule, import or direct query
- {{model_and_calculations}}: joins, calculated fields, custom SQL
- {{constraints}}: permissions, refresh windows, what cannot change
- {{what_you_have_tried}}: optional
Instructions
- Ask for any missing inputs, then restate the dashboard, its users and the symptom in two sentences.
- Rank likely causes from most to least probable across query, data model, visual and refresh layers.
- For each cause, give why it fits, the fix, the effort, and how to verify the gain.
- Split the list into quick wins (same day, low risk) and structural changes (needs review or a ticket).
- List what to measure before and after, such as load time or query duration.
- Flag assumptions and anything needing vendor documentation or the data engineering owner.
Output format Markdown sections: Symptom summary; Ranked causes table with columns Cause, Why it fits, Fix, Effort, How to verify; Quick wins; Structural changes; Measure before and after; Assumptions and checks. Under 600 words. Plain language, short concrete steps, no long code blocks. Leave out generic advice like "reduce data volume" with no specific step.
Guardrails
- Do not invent table names, metric names or platform limits; ask instead.
- Flag any fix that could change numbers stakeholders see, and suggest checking a figure someone already trusts.
- Confirm with vendor documentation and the data engineering owner before changing the model, refresh or source query.
Example bi_tool: Power BI; data_source: Snowflake star schema; symptom_and_timing: sales page takes 45 seconds, worst in the morning; constraints: Pro licence, cannot change the source query.