Resource
Executive Summary Prompt
Summary, Why It Matters, What Happens Next — the executive summary contract for readers who will never open the source.
Product Feedback Summary Prompt
Key Findings, Recommendations, Open Risks — collected feedback compressed for the roadmap discussion, with recommendations the source actually supports.
Overview
Feedback summaries drift in a specific direction: the model's own product opinions leak into the recommendations. This setup compresses collected feedback into Findings & Recommendations structure — Key Findings with numbers as written, Recommendations the source makes OR directly supports (nothing freelanced), Open Risks for what the feedback leaves unresolved — under High Fidelity with Important Quotes, so the feedback's strongest statements arrive in customers' own words. Built for the quarterly roadmap discussion, where the summary competes with opinions.
How to use this resource
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Compress the collection, not the items
Run it on the assembled feedback set — per-item processing is classification and extraction territory.
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Audit the Recommendations section
"Makes or directly supports" is the leash — any recommendation without a finding behind it is a red flag.
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Bring quotes to the meeting
A verbatim customer sentence ends roadmap debates that summaries can't.
Why This Works
- Findings/Recommendations separation keeps evidence and advice distinct
- The source-support rule stops the model's product opinions from leaking in
- Open Risks gives the skeptics a section, which keeps the summary credible
Best for
- Product teams running quarterly planning on real input
- Feedback collections too large for everyone to read
- Discussions where "customers want X" needs receipts
Not for
- Categorizing individual feedback messages — that's the Data Classification Prompt
- Mining individual reviews into fields — that's the Extraction Prompt Generator
Use cases
- Preparing the feedback digest for roadmap planning
- Keeping recommendations tied to what customers actually said
- Quoting the strongest customer statements verbatim
FAQ
How do I keep an AI feedback summary from adding its own product opinions?
The FIDELITY RULES restrict the ## Recommendations section to "recommendations the source makes or directly supports" — nothing freelanced — and forbid outside knowledge, so any recommendation without a finding behind it is a red flag you audit. The Structured Summary Prompt builds this; you still run it in your assistant and check that advice traces to the source, since the rule guides but doesn't guarantee the model's behavior.
Will the summary quote customers directly or paraphrase everything?
The QUOTE RULES require direct quotes for the most important statements, exactly in quotation marks and attributed to their speaker or section, so the strongest lines arrive in customers' own words for the roadmap discussion. Numbers, dates, and names are preserved as written. It compresses an assembled feedback set into three fixed sections — it isn't for categorizing or mining individual messages.
More resources from Structured Summary Prompt
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Structured Summary Prompt — the Anatomy
The blocks a reliable summary prompt needs: source guidance, a fixed section skeleton, length budgets, fidelity rules, and quote handling.
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Stop AI Summaries from Making Things Up
The fidelity ladder: Balanced, High, Strict — and the six-rule battery that keeps a summary inside its source.
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Extract Data From Text with AI
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
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Fix a Vague Prompt
'Make it good', 'be detailed', 'keep it interesting' — vague prompts get vague output. The fix is mechanical: replace every fuzzy word with a checkable instruction.
Related tools
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Structured Summary Prompt
Build summary prompts with fixed sections, length caps, and no-invention fidelity rules.
Projects that use this resource
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Build an AI Support Agent with AI
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
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Build a Customer Support System with AI
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.
Workflows that use this resource
Workflow
AI Customer Feedback Analysis Workflow
Turn a pile of reviews, surveys, or support comments into themes and priorities — extract the real signal, classify it by theme and sentiment, then summarize what's worth acting on.