Back to prompt library
Prompt libraryChat Prompt

User Feedback Theme Map Chat

Cluster user feedback into themes, sample quotes, product implications, and follow-up research questions.

ResearchCustomerProduct
Preview

Chat Prompt

Recommended model

Gemini 2.5 Pro

Output format

Feedback theme map

Preview

Chat Prompt

chat thread

Three users mention confusing model names. One agency asks for team history. Two creators say they just want faster image retries.

Theme: model choice clarity. Sample quote: users mention confusing model names. Frequency hint: three notes, likely worth validating. Product implication: add plain-language model guidance near the run panel. Follow-up question: does guidance improve first successful generation for new users. Edge case: team history request may belong to agency workflow research.

Output

Theme / sample quote / frequency hint / product implication / follow-up question

Helps product teams turn raw comments into researchable themes.

Full prompt

User Feedback Theme Map Chat

Cluster user feedback into themes, sample quotes, product implications, and follow-up research questions.

Recommended model: Gemini 2.5 ProOutput format: Feedback theme map
Full prompt
Chat Prompt
You are a user research lead organizing raw feedback. Cluster feedback without flattening important exceptions. Return the answer with: theme, sample quote, frequency hint, product implication, follow-up question. Do not turn one quote into a broad trend. Keep edge cases visible when they change the decision.

Usage notes

Include feedback source, segment, and date. Keep small sample sizes visible.

Prompt FAQ

Before you use this prompt

Quick checks for inputs, model fit, and how to adapt the template without weakening the result.

When should I use User Feedback Theme Map Chat?

Cluster user feedback into themes, sample quotes, product implications, and follow-up research questions. Use it when you already have notes, constraints, or a rough draft and need a structured next step a team can review.

What should I include before running it?

Include the source material, audience, constraints, key facts, and boundaries the answer must not invent. The output is organized as Theme / sample quote / frequency hint / product implication / follow-up question.

Thread preview

Three users mention confusing model names. One agency asks for team history. Two creators say they just want faster image retries.
Theme: model choice clarity. Sample quote: users mention confusing model names. Frequency hint: three notes, likely worth validating. Product implication: add plain-language model guidance near the run panel. Follow-up question: does guidance improve first successful generation for new users. Edge case: team history request may belong to agency workflow research.

Output

Theme / sample quote / frequency hint / product implication / follow-up question

More prompts in this mode

chat thread

We want to build an AI assistant for small ecommerce teams that turns product photos into campaign assets.

Problem hypothesis: small ecommerce teams lose time turning raw product photos into channel-ready campaign assets. Riskiest assumptions: photo quality is high enough, teams trust AI asset variation, and review time is the real bottleneck. Research questions: who owns campaign asset creation, where revisions stall, and what quality bar blocks publishing. Validation plan: interview 5 operators, test 3 prompt-led asset flows, and compare time-to-first-approved asset. Decision gate: continue only if teams can reach a publishable draft faster than their current workflow.

chat thread

We are exploring a new AI notes product for solo consultants. Help me turn this into a research brief.

Objective: define whether solo consultants need an AI notes workspace or a lighter client-follow-up layer. Working assumptions: they already capture notes, but synthesis and next-step drafting are inconsistent. Audience: solo consultants with recurring client calls and limited operations support. Key questions: which notes become billable work, what gets lost after calls, and where CRM tools feel too heavy. Research plan: run 6 interviews, review 10 recent call-note workflows, and test one follow-up brief prototype.

chat thread

Here is the outline for our AI product landing page. Tell me what is unclear before we design it.

Core promise: visible, but still framed as a feature rather than a concrete user outcome. Unclear point: the page does not explain who gets value first or what workflow changes after signup. example gap: add before-after examples, model output samples, and one short trust signal near the hero. CTA issue: the primary action appears after too much explanation; move a use-oriented CTA closer to the quick-use section. Revision plan: sharpen the hero, add outcome cards, then rewrite objections before polishing visuals.