Product

Categorize Customer Feedback with AI

Praise, Complaint, Feature Request, Bug Report, Question — multi-label feedback categorization where one message can carry three signals.

Overview

Customer feedback refuses to stay in one box: "love the app, but exports crash, and could you add CSV?" is Praise, Bug Report, and Feature Request in one message. This setup runs Multiple Labels mode, whose edge-case rules change accordingly: return every label that genuinely applies, strongest first — but a merely mentioned topic doesn't earn its label. The definitions split the classic confusions: Complaint is dissatisfaction without a requested remedy, Bug Report is something behaving incorrectly, Feature Request is a wish for new capability.

Workflow

One message per call
Multi-label works per message — the labels list comes back ordered by strength.
Trust the mention threshold
"A merely mentioned topic does not earn its label" keeps drive-by mentions from inflating your counts.
Watch the Complaint/Bug border
Dissatisfaction without a remedy is Complaint; something behaving incorrectly is Bug Report. The definitions decide, not the model's mood.

Why This Works

Multi-label mode matches how feedback actually arrives — bundled
Strongest-first ordering gives downstream consumers a primary signal anyway
Exclusion-shaped definitions stop the same sentence from earning two labels

Best for

Product teams processing feedback at volume
Mixed-signal messages that single-label classification mangles
Pipelines that route bugs and wishes to different boards

Not for

Extracting the quotes and topics themselves — that's the Extraction Prompt Generator
Overall sentiment scoring — that's a single-label problem with its own setup

Use cases

Feeding a feedback board where one message can hit three categories
Separating bug reports from complaints automatically
Counting feature demand without losing it inside mixed messages

Customize This Resource

Opens this setup in Data Classification Prompt. Generate to get the full classification prompt — then adjust the labels, ambiguity mode, and confidence output.

Open in Data Classification Prompt

Prompt Template

TASK
Categorize customer feedback messages for the product feedback board.
Assign one or more of the labels defined below to the text in the input.

LABELS
- Praise: Positive feedback expressing satisfaction; no action requested.
- Complaint: An expression of dissatisfaction that does not request a specific remedy.
- Feature Request: A suggestion for a new capability or an improvement to an existing one.
- Bug Report: A description of something behaving incorrectly or not working.
- Question: A request for information or guidance — not a problem report.

CLASSIFICATION RULES
- Read the entire text before deciding — the deciding signal may come late.
- Judge by content and intent, not by tone, length, or formatting.
- Match the text against the definitions, not just the label names.
- Use only the labels defined above — never invent a new label.

EDGE CASE RULES
- If the text fits more than one label, return every label that applies.
- Order the labels by how strongly each applies, strongest first.
- A merely mentioned topic does not earn its label — it must be a real subject of the text.

AMBIGUITY POLICY
- Choose a label only when its definition genuinely covers the text.
- When torn between two labels, re-read both definitions and pick the closer one — do not guess from the names.

OUTPUT FORMAT
Return your classification as a single valid JSON object with a "labels" array.

EXAMPLE OF A VALID RESPONSE
{
  "labels": ["Praise", "Complaint"]
}

OUTPUT RULES
- Return only the JSON object — no text before or after it.
- Do not wrap the output in markdown code fences.
- Return the label text exactly as defined above — no paraphrasing, no abbreviations, no new labels.

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