Cadastre-se grátis

Analyze user feedback without losing evidence

Autor: AILesson6 min de configuraçãoTestado com:ChatGPTClaudeGeminiRevisado em: 2026-08-27

Resposta rápida

Group themes while keeping quotes, counts, and uncertainty attached. Forneça: Feedback entries, Analysis question. Resultado esperado: Evidence-backed themes and a list of claims the data cannot support.

1

Adicione seu contexto

Seu texto permanece neste navegador. O AILesson Prompts não o envia para um modelo ou servidor.

2

Seu prompt

Campos não preenchidos permanecem visíveis como marcadores de posição, para que você ainda possa copiar e editar o prompt

Analyze the numbered feedback entries below to answer this question:
[question]

Feedback:
[feedback]

Rules:
- Preserve the entry number for every piece of evidence.
- Do not infer prevalence beyond this sample.
- Separate direct observation, interpretation, and hypothesis.
- Do not merge comments with meaningfully different causes.

Produce:
1. A coding table with entry number, code, sentiment, and evidence excerpt
2. Themes with supporting entry numbers and counts
3. Contradictions and outliers
4. Product implications labeled by confidence
5. Claims this dataset cannot support
6. The next evidence we should collect
Experimente no Playground
Privado por padrãoA montagem do prompt acontece localmente no seu navegador. Evite colocar informações confidenciais em qualquer serviço de IA, a menos que sua organização permita.

Da entrada ao resultado

Um exemplo prático

Veja como um contexto concreto transforma esta receita em um resultado útil

Entrada real

Feedback entries
1. “The reminders are useful, but getting one for every comment is exhausting. I would rather receive a daily digest.” 2. “Keep the immediate alerts for failed imports. One saved me from presenting outdated numbers to a client.” 3. “Push notifications sometimes arrive about 20 minutes late on my phone.” 4. “The email summary is helpful, although the same task can appear twice.” 5. “I turned off all notifications after a busy project generated dozens of comment alerts.” 6. “I need notification settings per project. The current all-or-nothing switch is too coarse.”
Analysis question
What notification changes should the product team prioritize before the next beta?

Exemplo de saída

1. Coding table

EntryCodeSentimentEvidence
1Comment overload; digest preferenceMixed“one for every comment is exhausting”
2Critical alerts create valuePositive“saved me from presenting outdated numbers”
3Mobile delivery delayNegative“about 20 minutes late”
4Useful summary; duplicate contentMixed“same task can appear twice”
5Alert volume causes opt-outNegative“turned off all notifications”
6Missing project controlsNegative“all-or-nothing switch”

2. Themes

  • Control and alert volume: entries 1, 5, 6 (3 entries).
  • Delivery quality: entries 3, 4 (2). Delay and duplication should remain separate causes.
  • Value of selective immediacy: entry 2 (1). A failed-import alert was useful.

Counts describe only these six entries, not the wider user base.

3. Contradictions and outliers

Entry 1 prefers a digest for comments, while entry 2 values immediate failed-import alerts. These needs are compatible if urgency varies by event type. Entry 3 is the only delivery-delay report.

4. Product implications

  • High confidence within this sample: offer controls by project or event type.
  • Medium: investigate duplicate summaries and mobile delays separately.
  • Medium: preserve immediate critical alerts while testing digests for routine activity.

5. Unsupported claims

The sample cannot establish prevalence, platform-wide reliability, whether digests reduce opt-outs, or which change improves retention.

6. Next evidence

Collect event-level delivery logs, settings and opt-out behavior, then test digest frequency and per-project controls with a larger, segmented beta sample.

Por que isso funciona

  1. 1

    Entry IDs keep synthesis connected to raw evidence.

  2. 2

    Separating observations from hypotheses limits overclaiming.

Verifique o resultado

  • Can every theme be traced to specific entries?

  • Are sample limitations stated clearly?

Use com confiança

Perguntas frequentes

Respostas práticas sobre quando usar esta receita, o que fornecer e onde a revisão humana ainda é importante

What should I prepare before using “Analyze user feedback without losing evidence”?

For “Analyze user feedback without losing evidence,” prepare Feedback entries and Analysis question. 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 “Analyze user feedback without losing evidence” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—Evidence-backed themes and a list of claims the data cannot support—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 “Analyze user feedback without losing evidence”?

The published test record for “Analyze user feedback without losing evidence” lists ChatGPT, Claude, and Gemini as of 2026-08-27. 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.

Mais maneiras de explorar

Onde esta receita se encaixa

Prompts de textoPesquisa e análiseFeedback e revisãoPara gestoresDa evidência à decisão

Mantenha o trabalho em andamento

AILesson · Cursos recomendados

Seu próximo passo: use a IA na prática

Passe de entender a IA para fazer as coisas acontecerem. Pratique como criar prompts, revisar e melhorar resultados por meio de lições interativas para o trabalho e o dia a dia.

Explore todos os cursos
Capa de IA para iniciantes

15 lições interativas

IA para iniciantes

Inicie uma conversa útil com IA, melhore a resposta, confira o que importa e saiba quando o próximo passo é seu.

Explore este curso
Capa de Fundamentos do ChatGPT

10 lições interativas

Fundamentos do ChatGPT

Aprenda a usar o ChatGPT por meio de três fluxos de trabalho práticos: conclua uma primeira conversa, trabalhe a partir de um arquivo e use Projects para levar um plano verificado a uma nova conversa.

Explore este curso
Capa de Como a IA generativa funciona

7 lições interativas

Como a IA generativa funciona

Entenda como chats comuns de IA aprendem padrões da linguagem, usam mensagens atuais, criam respostas e incorporam fontes e ferramentas.

Explore este curso