Open-Ended Response Cluster Summarizer
For researchers summarizing large sets of open-ended survey comments into themes and takeaways.
Best for these models
๐ The Prompt
๐ Prompt available in download
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Download PromptVariables to fill in
{{SURVEY_QUESTION}} โ Replace with your input {{AUDIENCE}} โ Replace with your input {{RESPONSE_COUNT}} โ Replace with your input {{SENTIMENT_LENS}} โ Replace with your input {{RESPONSES}} โ Replace with your input About this prompt
This template converts ChatGPT or Claude into a fast open-ended response summarizer that groups free-text survey answers into clear themes. It is useful when you have hundreds of comments and need a reliable first-pass synthesis without losing the voice of respondents. The prompt asks the model to preserve representative phrasing while still compressing the data into readable clusters.
It is a strong fit for customer experience teams, HR analysts, and researchers who collect large volumes of qualitative feedback. The output usually includes theme names, frequency cues, sample quotes, and a short interpretation of what the comments suggest. This helps teams move from raw text to action faster. A clean comment synthesis also makes it easier to share results with non-research stakeholders.
Customize the prompt by defining the survey question, audience, and any sentiment scale you want considered. You can also request separate clusters for praise, complaints, and suggestions. The model will return a structured summary that can be pasted into a report or slide deck. If needed, ask for a version optimized for executive readers or a deeper version for analysts. This keeps the template flexible while still supporting consistent survey text analysis.
Key features
- Clusters free-text comments into readable themes
- Preserves representative respondent language and quotes
- Highlights complaints, praise, and suggestions separately
- Estimates prevalence without overclaiming precision
- Produces a stakeholder-friendly survey summary
Best for
- โ Customer experience teams analyzing feedback forms
- โ HR analysts reviewing employee comments
- โ Researchers summarizing large open-text datasets
Tips
- ๐ก Tell it whether you want sentiment or topic clustering first.
- ๐ก Provide the exact survey question to keep summaries on target.
- ๐ก Ask for a separate section on outliers if edge cases matter.
What you'll get
A theme table with cluster names, example quotes, rough prevalence, and an interpretation section. It also includes notable outliers and suggested actions so the summary is immediately useful for reporting.
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