Multi-Label Product Feature Classification Prompt Template

This prompt is designed for product teams and data engineers who need to transform unstructured feedback—such as user reviews, NPS verbatims, support tickets, and sales call transcripts—into structured, queryable metadata at scale. The core job-to-be-done is assigning multiple feature labels from a predefined, stable taxonomy to a single text input. This enables downstream analytics like calculating feature-level sentiment trends, identifying co-occurring feature complaints in bug reports, or routing feedback to the correct product manager. The ideal user is someone building a data pipeline who needs consistent, machine-readable labels, not a one-off analysis. You must provide a controlled taxonomy of features (e.g., `[

search

notifications

billing

onboarding

This template is designed to be pasted directly into your system instructions for a multi-label classification task. It forces the model to operate within a strict taxonomy, apply a configurable confidence threshold, and handle edge cases like co-occurring features or text that matches no label. The square-bracket placeholders must be replaced with your specific domain values before use.

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SYSTEM INSTRUCTION:
You are a product feedback classifier. Your task is to analyze the provided [INPUT] text and assign one or more feature labels from the [TAXONOMY] list below. You must follow these rules exactly:

1.  **Taxonomy:** Only use labels from this list: [TAXONOMY]. Do not invent new labels.
2.  **Threshold:** Assign a label only if your confidence is greater than [CONFIDENCE_THRESHOLD] (e.g., 0.7).
3.  **Co-occurrence:** If the text discusses multiple features, assign all applicable labels that meet the threshold.
4.  **No Match:** If no label meets the threshold, or the text is not about product features, return an empty list for `assigned_labels`.
5.  **Evidence:** For every assigned label, provide a brief, verbatim quote from the [INPUT] text as `evidence_span`.
6.  **Output Format:** Return your response strictly as a JSON object matching the [OUTPUT_SCHEMA] below. Do not include any text outside the JSON object.

[OUTPUT_SCHEMA]:
{
  "assigned_labels": [
    {
      "label": "string",
      "confidence": "number (0.0 to 1.0)",
      "evidence_span": "string"
    }
  ],
  "unmatched_rationale": "string (optional, provide a brief reason if no labels were assigned)"
}

[TAXONOMY]:
- feature_a
- feature_b
- feature_c

[CONFIDENCE_THRESHOLD]: 0.7

[INPUT]:
{{user_input}}

To adapt this template, start by populating the [TAXONOMY] with your specific feature list, ensuring labels are mutually exclusive where possible to reduce ambiguity. Set the [CONFIDENCE_THRESHOLD] based on your tolerance for false positives; a higher threshold (e.g., 0.85) is safer for automated downstream actions, while a lower one (e.g., 0.6) is better for human-in-the-loop review queues. The [OUTPUT_SCHEMA] can be extended with fields like feature_version or sentiment if your analytics pipeline requires them. For high-stakes product decisions, always route low-confidence outputs to a human review stage before they enter your product roadmap database.

Integrating the Multi-Label Product Feature Classification prompt into an application requires more than a single API call. The prompt is designed to be a stateless function within a larger pipeline: it receives a text input and a taxonomy, and it returns a structured set of labels with confidence scores. The application layer is responsible for managing the taxonomy, enforcing the confidence threshold, handling model failures, and deciding what happens to the output next—whether that's writing to an analytics database, triggering a notification, or routing to a human review queue.

Start by defining a strict output contract. The prompt template expects a JSON response containing an array of label objects, each with a label string and a confidence float between 0.0 and 1.0. Before the response touches any downstream system, validate it against this schema. Reject any response where confidence is not a number, where labels are not strings from the provided taxonomy, or where the array is missing entirely. A simple JSON Schema validator at the application boundary prevents malformed outputs from corrupting your feature-tagging database. If validation fails, implement a retry loop with a maximum of two additional attempts, appending the validation error message to the prompt context so the model can self-correct.

The confidence threshold is the primary control surface for precision-recall trade-offs. Configure a MIN_CONFIDENCE parameter (start at 0.7) in your application, not in the prompt. After validation, filter out any labels below this threshold. Labels that fall below the line should be logged separately with their full context—this data is invaluable for calibrating the threshold later. For high-stakes workflows where missing a feature label has significant downstream cost (e.g., regulatory feature flags), route low-confidence predictions to a human review queue rather than discarding them. The prompt's [CONSTRAINTS] placeholder should include the current threshold value so the model can calibrate its own confidence expressions.

Model choice matters for this task. Classification prompts with large taxonomies (50+ labels) benefit from models with strong instruction-following and structured output capabilities. If you're classifying high volumes of feedback, consider a two-tier architecture: use a smaller, cheaper model for the initial classification pass, and escalate to a larger model only when the smaller model's confidence falls below a second, higher threshold. Log every classification decision—input text, model version, prompt version, raw response, validated labels, and final filtered labels—so you can measure drift, compare model performance, and debug misclassifications without replaying production traffic.

Avoid wiring this prompt directly to a user-facing dashboard without a review buffer. Fresh taxonomies, new product features, and shifting customer language will cause the model to make mistakes that look plausible. Build a sampling review pipeline that surfaces low-confidence and high-impact classifications for human verification. Use those human verdicts to update your few-shot examples in the prompt's [EXAMPLES] placeholder and to refine your taxonomy definitions. The prompt is the runtime artifact; the harness around it—validation, thresholding, logging, review, and feedback loops—is what makes it production-grade.