Paste the raw statistical results — p-values, confidence intervals,
effect sizes, coefficients — along with who you're presenting to, and
this prompt converts them into language an executive audience can
actually use. It produces a 3-sentence jargon-free executive summary, a
"so what" section spelling out what should change because of the
numbers, a plain-language confidence statement instead of "p < 0.05," a
magnitude analogy that makes the effect intuitive, honest caveats, and a
description of one chart that would communicate the finding.
Paste in raw analysis results — tables, statistics, key findings — along
with the audience, tone, and target length, and this prompt builds a
narrative instead of a results dump.
It opens with the original question, walks through evidence in logical
order, builds toward the insight rather than front-loading it, addresses
the obvious counterarguments, and closes with a recommendation and
confidence level, marking where charts should sit with [CHART:
description] tags.
Paste 5-8 key findings with their supporting numbers, the reader's role,
and what prompted the analysis, and this prompt turns them into a
summary an executive will actually read.
- One-paragraph bottom line stating the single most important takeaway
- 3-5 findings, each with a concrete "so what" implication
- Specific recommended actions, not vague advice like "invest more"
- Honest risks, caveats, and suggested next-step analysis
- All jargon-free and kept under 500 words
Describe the database engine, the available tables with their columns
and relationships, the analysis you need in plain English, and whether
to optimize for large tables or readability, and this prompt writes the
production-ready SQL query to get there. It handles nulls sensibly, uses
CTEs to keep complex logic readable, comments any non-obvious steps, and
returns business-friendly column names instead of raw
database_column_names — useful whenever you know the analysis you want
but not the SQL to write it.
Provide a finalized theme name, its component codes, and 2-3 example
passages, and this prompt drafts the 150-200 word description that
theme needs in a results section.
It states the analytical claim the theme is making rather than just
naming its topic, ties that claim back to the codes underneath it, and
suggests what kind of illustrative quotes would best support it — run
once per theme, then assemble the outputs in order.
Paste in one analysis summary — the findings and recommendations — and
this prompt rewrites it three times for three depths of reader.
- A 200-word C-suite version: bottom line and decision, no methodology
- A 500-word manager version: team-level implications and actions
- An 800-word analyst version: full methodology and limitations
- All three carry the same core insight at different depth and vocabulary
List the filter dimensions users can apply, the key metrics shown, the
drill-down hierarchy, the comparison periods needed, the database
engine, and the relevant tables, and this prompt writes the
parameterized SQL query layer behind an interactive dashboard. Each
panel's query accepts filter parameters using {{parameter_name}} syntax,
returns data shaped for its visualization, is written for sub-second
interactive response, and handles edge cases like empty filters meaning
"all data" or invalid date ranges.
Generates a structured batch of email subject lines for testing, grouped by psychological angle rather than random variation.
- Inputs: email topic, audience, main message, desired action, brand voice, and words to avoid
- Produces: 20 subject lines grouped by angle, preview text for the 5 strongest, and a testing hypothesis per group
- Flags: spam-risk wording and recommends one A/B test pair to run first
- Built for email marketers who want a defensible reason behind every subject line, not just volume