Turn my analysis findings into a clear recommendation
Converts findings into options, a recommended choice, trade-offs, assumptions, and success metrics a decision-maker can act on.
The prompt
Turn my analysis findings into a clear, data-driven recommendation that a decision-maker can approve, reject, or adjust.
Findings: [PASTE key results, with numbers]
Decision to be made: [e.g. "whether to discontinue product line B"]
Decision-maker and what they care about: [e.g. "COO - cost and service levels"]
Options on the table: [OPTIONAL - list them, or leave blank and propose them]
Constraints: [budget, timing, people, policy]
Steps:
1. Restate the decision as a single question.
2. List 2-4 realistic options, including "do nothing" or "wait for more data".
3. For each option, show the expected impact using my numbers, the cost or effort, the main risk, and how reversible it is.
4. Recommend one option and explain why it beats the others, tied directly to specific findings.
5. State the confidence level and the assumptions that, if wrong, would change the recommendation.
Return:
1. The recommendation in one sentence: what, by when, and the expected result.
2. Options table: Option | Expected impact | Cost or effort | Key risk | Reversible?
3. The three findings that drive the recommendation.
4. Assumptions, and what evidence would change the recommendation.
5. How we will know it is working: 2-3 metrics with a check-in date.
6. A 100-word version for an email or a Teams or Slack message.
Use only my figures; mark gaps as [ADD DATA]. This is decision support: the decision-maker owns the call, so present the trade-offs honestly rather than selling one option.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- PASTE key results, with numbers
- e.g. "whether to discontinue product line B"
- e.g. "COO - cost and service levels"
- OPTIONAL - list them, or leave blank and propose them
- budget, timing, people, policy
- ADD DATA
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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