Prompt: Executive Summary from Dense Research
A summarisation prompt for compressing dense research into an executive brief without flattening the key caveats, risks, or implications.
Use cases
Research & Analysis, Strategy & Planning
Platforms
Claude, GPT, Model-Agnostic
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Workspace-ready resource
Copy the raw material, then check the setup, output contract, and failure modes.
Summarise the supplied research for an executive reader who wants signal, not detail. Requirements:- Prioritise findings by decision relevance, not by source order.- Preserve uncertainty, caveats, and contradictory evidence.- Separate facts from interpretations.- Keep the summary concise but not misleading. Return:1. Executive summary in 5-7 bullets2. Most important supporting evidence3. Business implications4. Risks and caveats5. Open questionsWorkspace translation
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Run it once manually. If the same job appears twice, package it.
Quality bar
Before using this, check the contract.
What input does this require?
What output should it produce?
Where can it fail?
What should a human review?
Recommended path
When to Use This
Use this when someone senior needs the shape of the research quickly, but you do not want a shallow summary that removes the uncertainty that actually matters.
It works well for market scans, competitor analysis, customer interviews, internal research packs, and vendor evaluations.
Why It Works
The prompt focuses on decision relevance because dense research often contains interesting detail that is operationally unhelpful.
The separate risks and caveats section is what stops the executive summary from becoming overconfident. Without that, the model tends to smooth away the uncertainty.
How to Customise
Add a target audience like founder, head of sales, or product lead if you want the implications section framed more precisely.
If the research is especially messy, require a short evidence table under each key point.
Limitations
This prompt compresses. Compression always loses detail. It is useful for briefings, not as a substitute for the underlying research pack.
It also cannot resolve weak or conflicting source material by itself. Someone still has to judge the evidence.
Model Notes
Claude is particularly good at preserving nuance while staying concise.
GPT works well if you constrain the output length tightly. Model-agnostic overall as long as you keep the structure explicit.
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Related Guides
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