Turn raw data or a spreadsheet into a presentation narrative
Checks your raw data, then turns it into a sequence of slides, each with a finding headline, the right chart, and caveats.
The prompt
Turn my data into a presentation narrative.
Data: [PASTE the table, Excel export, or key metrics with time periods and units]
What the data is about: [e.g. "Q3 sales by region", "customer survey results"]
Audience: [WHO, and how comfortable they are with numbers]
Question the audience needs answered: [e.g. "Are we on track?", "Where should we invest?"]
Number of slides: [e.g. 5-8]
Steps:
1. Check the data first: list any gaps, odd values, mismatched periods, or small sample sizes that could mislead. Don't fix them silently.
2. Find the 3-5 findings that answer the audience's question. Rank by importance, not by the order of the data.
3. Separate what the data shows (fact) from what might explain it (hypothesis).
Return:
1. The big picture in 2 sentences: the answer to the audience's question.
2. Slide-by-slide: headline stating the finding in plain words with the number ("West region drove 70% of growth"), the chart type and exactly what it should plot (axes, series, what to highlight), and one line of speaker notes on why it matters.
3. A "so what" slide: implications and recommended next steps, labeled as my decision to make.
4. Caveats to state out loud and questions to expect.
Use only my numbers. Show any calculation you do (percent change, share, average) so I can check it in Excel. Never round in a way that changes the message.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Presentations & Public Speaking prompts
What to fill in
- e.g. "Q3 sales by region", "customer survey results"
- WHO, and how comfortable they are with numbers
- e.g. "Are we on track?", "Where should we invest?"
- e.g. 5-8
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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