Cluster feedback by underlying progress and breakdown while preserving source, variation, and uncertainty. 제공할 내용: Feedback corpus, Sample and quality context, Product decision context. 예상 결과: An evidence-linked opportunity map with themes, affected contexts, negative cases, confidence, risks, and next research.
1
맥락 추가
텍스트는 이 브라우저에 유지됩니다. AILesson Prompts는 이를 모델이나 서버로 보내지 않습니다.
2
프롬프트
채워지지 않은 필드는 플레이스홀더로 표시되므로 프롬프트를 복사하고 편집할 수 있습니다
Organize the supplied user feedback into evidence-bound opportunity areas, not a feature backlog.
Feedback records, IDs, dates, channels, roles, context, wording, versions, outcomes, and consent:
[feedback]
Window, bias, duplicates, exposure, missingness, taxonomy, prior themes, and incidents:
[quality]
Horizon, strategy, decisions, constraints, risk, priority criteria, excluded solutions, and reviewers:
[decision]
Validate unique records and distinguish unsolicited feedback, prompted answers, observed behavior, support issue, feature request, workaround, satisfaction, and researcher interpretation. Code situations, triggers, goals, current alternatives, breakdowns, consequences, constraints, and desired progress with source locators. Cluster by solution-independent opportunity, not shared keywords or requested features. For each area write a “people in context need progress because evidence” statement; list unique records and participants, roles and segments, supporting behaviors and quotes, negative and contradictory cases, known-incident overlap, severity and frequency evidence, confidence, strategic relevance, risks, and unknowns. Do not infer prevalence without a denominator, combine incompatible contexts, count repeat contacts as new users, or call an idea validated because people requested it. Rank only with supplied criteria and show sensitivity; otherwise provide a review order. Separate opportunity evidence from solution ideas and recommend the next smallest research or instrumentation step.
기본적으로 비공개프롬프트 구성은 브라우저에서 로컬로 이루어집니다. 조직에서 허용하지 않는 한 기밀 정보를 AI 서비스에 입력하지 마세요.
입력에서 결과까지
적용 예시
구체적인 맥락이 이 레시피를 바로 사용할 수 있는 결과로 바꾸는 방법을 확인하세요
실제 입력
Feedback corpus
Last 60 days: 42 unique support cases tagged reminder; 18 unique cases request fewer notifications; 8 interview participants from support recruitment. P01 and P03 could not identify an accepter in recent handoffs; P02 intentionally waited across timezone and disliked more email; P04 waited for known approval. Analytics: 31% of active workspaces had one Ready state over 48 hours, but event cannot distinguish intentional waiting. Incident INC-901 caused duplicate email for 11 of the 18 fewer-notification cases; fixed Aug 12. Record IDs and locators are available; one support customer contacted three times but is one case.
Sample and quality context
Support channels overrepresent problems; interviews are a convenience sample. Deduplicate by case and participant. Active-workspace denominator is known for analytics only. Versions missing in 20% of support cases. Feature-request prompts were used in two interviews. No causal link between long Ready and missed work.
Product decision context
Next-quarter goal is reduce avoidable handoff uncertainty without increasing notification burden. Options remain open; automatic email is not preselected. Review order weighs severity, strategic fit, evidence strength, and reach, but no numeric weights approved. Privacy and accessibility review required for concepts. Product and Research review together.
예시 출력
Opportunity 1 — make acceptance responsibility and next authority legible in ambiguous handoffs
Statement: when a handoff is ready but acceptance responsibility is not visible, acting roles need to identify who can accept and what happens next so they can route action without searching across people. Evidence: P01 and P03 recent behaviors and artifacts; medium confidence, two convenience-sample participants. Negative/variant cases: P02 intentionally waited and P04 knew approval was pending, so not every long Ready state belongs here. Strategic fit is high; reach is unknown. Next step: instrument accepter presence and recruit recent instances across support and non-support populations.
Opportunity 2 — distinguish expected waiting from unattended work
Statement: when Ready persists, roles need the reason and expected next event so they can decide whether to wait or intervene. Evidence: P02 timezone wait, P04 approval wait, plus analytics that detect duration but not reason. Medium confidence on variation, low on reach and consequence. Next step: taxonomy study and event-definition audit.
Opportunity 3 — control notification relevance and duplication
Evidence: 18 unique fewer-notification cases, but 11 overlap fixed duplicate-email incident INC-901. Post-fix residual is seven cases, with version missing in some records. Do not merge all 42 reminder-tag cases as support for more email; tags may represent requests, current failures, or discussion. Incident evidence is separate from the broader opportunity. Next step: recode remaining cases by trigger, channel, desired action, and post-fix version.
Review order: first opportunities 1 and 2 because they align with reducing uncertainty without selecting a notification solution; then assess residual notification control after incident cleanup. Numeric ranking is not justified without approved weights and comparable reach. The 31% workspace metric describes an event condition only, not avoidable delay or affected-user prevalence. Automatic email remains one unvalidated idea and requires privacy/accessibility review.
효과가 있는 이유
1
Solution-independent clustering preserves the problem space before feature selection.
2
Negative cases and channel bias keep noisy feedback volume from becoming false certainty.
결과 확인
Does each opportunity combine compatible contexts and trace to unique evidence?
Are requests, incidents, behavior, interpretation, and solution ideas separate?
Are prevalence, priority, and confidence proportional to sample quality and criteria?
안심하고 사용하세요
자주 묻는 질문
이 레시피를 언제 사용해야 하는지, 무엇을 제공해야 하는지, 그리고 어떤 부분에서 사람의 검토가 여전히 중요한지에 대한 실용적인 답변
What should I prepare before using “Organize user feedback into opportunity areas”?
For “Organize user feedback into opportunity areas,” prepare Feedback corpus, Sample and quality context, and Product decision context. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.
When is the “Organize user feedback into opportunity areas” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—An evidence-linked opportunity map with themes, affected contexts, negative cases, confidence, risks, and next research—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.
Which AI tools have recorded tests for “Organize user feedback into opportunity areas”?
The published test record for “Organize user feedback into opportunity areas” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.
ChatGPT
