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What It Does

  • Classifies text using AI into categories (or “keys”) that you define.
  • Supports single or multi-label classification, depending on your settings.
  • Optional reasoning helps explain why the label was chosen.
  • Fully configurable with custom keys, instructions, and optional column naming.
  • Auto-resolves naming conflicts so your original data stays safe.

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🏁 Getting Started

Classifier node configuration screenshot
1

Add the Node

Drop the Classifier node anywhere in your playbook.
2

Choose Input Column

Select the column that contains the text to classify. Use Insert Input or type @ to reference earlier steps.
3

Write Your Instructions

Optionally, guide the model with a prompt like “Classify this as pricing, support, or product.”
4

Define Keys

Add labels for the AI to pick from. Each key can include:
  • A name (e.g. support)
  • A description to help improve accuracy
5

Set Preferences

  • Toggle Include Reason to explain why the label was picked
  • Toggle Allow Multiple Classes to enable multi-label tagging
  • Optionally rename the output column
6

Run the Node

Each row will now return a classification result (and optionally a reason).

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Inputs

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🛠️ Required Fields

  • Text Column (✅)
    Select the column you want to classify — e.g., @feedback, @summary, etc.
  • Classification Keys (✅)
    Define the labels you want applied. Each key includes:
    • A name (e.g. product, pricing)
    • An optional description (e.g. “Mentions product bugs or roadmap features”)

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🎯 Optional Fields

  • Instructions (⚪️)
    Helps the AI understand how to apply your keys. Example: “Tag this as product, pricing, or support.”
  • Include Reason (⚪️)
    Adds a second column with short explanations.
    Adds 1 extra credit per row.
  • Allow Multiple Classes (⚪️)
    Enables multiple labels per row.
    Returns values as arrays like ["product", "pricing"].
  • Output Column Name (⚪️)
    Customize the result column. Defaults to CLASS.
    Auto-suffixed if there’s a conflict (CLASS_1, etc).

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Output

The node adds:
Output ColumnDescription
CLASS (default)The predicted label(s).
CLASSIFICATION_REASON(Optional) Explains why each label was chosen.
Classifier node output example
If multi-label is on, values appear as arrays like ["support", "pricing"].

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🚀 Example Use Cases

Use CaseSetup Example
Feedback TaggingClassify @feedback into product, pricing, support, other
Sentiment AnalysisClassify @review_text as positive, neutral, negative
Intent DetectionTag @chat_log for buying intent, support, info-seeking
Content ClassificationLabel @summary into news, promo, update, other

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✨ Pro Tips

Use short descriptions for each key — it makes classification more accurate and predictable.
Add fields using @ or the Insert Input button — avoids typos and ensures clean references.
Turn on Allow Multiple Classes when text may touch multiple themes.
Use a custom output name like tag or intent_label to make downstream logic clearer.

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⚠️ Important Considerations

Multi-class output returns arrays like ["product", "support"]. Plan downstream filters accordingly.
Include Reason increases cost by +1 credit per row.

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🛠 Troubleshooting & Gotchas

SymptomLikely CauseFix
Empty result columnNo keys providedAdd at least one classification key
Same label for all rowsInstructions too vagueAdd clearer rules or improve key descriptions
CLASS_1, CLASS_2, etc.Name conflict in outputRename the output column in config

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📝 FAQ

Yes — toggle Allow Multiple Classes to get array outputs like ["pricing", "product"].
Adds a second column explaining why the label was chosen. Great for review or debugging.
There’s no hard limit, but 3–10 keys works best for clarity and model accuracy.

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💰 Pricing

ModeCredit Cost per Row
Classification only1 credit
With Reason2 credits
Only rows that receive a result are charged. Enabling Include Reason adds +1 credit per row.

Classify anything — feedback, intent, topic, or theme — and make your data more actionable with just a few clicks. 🏷️⚡