Prompt Engineering

Text Classification Prompt — the Anatomy

The blocks a reliable classification prompt needs: defined labels, classification rules, edge-case rules, an ambiguity policy, and a confidence contract.

Open in Data Classification Prompt

Overview

A classification prompt that just lists label names leaves every real decision to the model. The reliable anatomy has five blocks: LABELS with one-sentence definitions (the classifier itself), CLASSIFICATION RULES (judge content not tone, match definitions not names), EDGE CASE RULES (what to do when two labels fit), an AMBIGUITY POLICY, and a CONFIDENCE contract. This resource loads a product-request taxonomy — New Feature, Improvement, Integration, Performance, UX — where the definitions carry the entire New-Feature-vs-Improvement distinction.

How to use this resource

Read the blocks in order

Labels define, rules constrain, edge cases resolve ties, ambiguity handles misfits, confidence enables routing — each block closes one failure mode.
Study the New Feature / Improvement border

"Does not have at all today" vs "should work better" — one adjective pair does what three paragraphs of instructions wouldn't.
Swap in your own taxonomy

Replace the labels with yours; known label names get library definitions automatically, domain-specific ones need your sentence.

Why This Works

Definitions turn labels from words into checkable rules
Tie-breaking rules ("prefer the more specific definition") remove the model's discretion where it's least reliable
Each block is short because it does one job — no rule soup

Best for

Anyone whose classification prompt is currently a bare label list
Taxonomies with adjacent categories that models confuse
Teams standardizing classification across many prompts

Not for

Extracting values from the text — extraction and classification are different tools
Open-ended tagging without a fixed label set — this anatomy assumes a closed set

Use cases

Learning the structure before building your own classifier prompts
Auditing an existing classification prompt against the five blocks
Classifying product requests by type for roadmap planning

FAQ

What does the confidence level in this classification prompt actually mean?

It's the model's self-reported certainty about the fit, not a computed score. The CONFIDENCE block defines high as the text unambiguously fitting the definition, medium as a good fit with minor uncertainty, and low as the best available choice among the labels. It ships in the JSON alongside the label. The data-classification-prompt writes this contract; run it in your own assistant and treat low-confidence labels as needing human review.

How does the prompt keep New Feature and Improvement from being confused?

The distinction lives in the LABELS definitions: New Feature is "a capability the product does not have at all today" versus Improvement, "an existing capability that should work better". The CLASSIFICATION RULES then force matching against definitions, not names, and the EDGE CASE RULES break ties toward the more specific definition. You swap in your own taxonomy; the generator derives the structure, and you verify the labels.

Can I use this to pull values like dates or names out of the text?

It can't. This assigns exactly one label from a closed set; it doesn't pull values, and the notFor separates extraction and classification as different tools. This anatomy assumes a fixed LABELS set and returns a JSON object with a "label" and a "confidence" key, nothing extracted from the source itself. For fields like dates or names, use an extraction prompt instead.

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

Copy it as-is, or use Open in Data Classification Prompt to load it pre-filled and customize it with your own context.

TASK
Classify product requests by type for roadmap planning.
Assign exactly one of the labels defined below to the text in the input.

LABELS
- New Feature: A capability the product does not have at all today.
- Improvement: An existing capability that should work better, faster, or easier.
- Integration: A request to connect the product with another tool or service.
- Performance: Speed, latency, or resource-usage problems and requests.
- UX: Friction in using the product — confusing flows, unclear UI, hard-to-find features.

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, choose the one matching its primary purpose — what the writer mainly wants.
- If two labels fit equally, prefer the more specific definition over the more general one.

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.

CONFIDENCE
- With each label, report a confidence level: high, medium, or low.
- high = the text unambiguously fits the definition; medium = a good fit with minor uncertainty; low = the best available choice among the labels.
- Confidence reflects your certainty about the fit — it is self-reported, not computed.

OUTPUT FORMAT
Return your classification as a single valid JSON object with a "label" key and a "confidence" key.

EXAMPLE OF A VALID RESPONSE
{
  "label": "New Feature",
  "confidence": "high"
}

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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