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NLP & Extraction

Intent Detection vs Text Classification

The difference between classic text classification and intent detection with entity extraction, with examples of when each is the right tool.

Side by side

Intent Detection vs Text Classification

Option A

Intent Detection

Identifies what the user wants to do and extracts the details needed to do it, returned as JSON with a confidence score.

Strengths

  • Goal plus extracted entities
  • Confidence threshold routing
  • Multi-intent support
  • Ready for chatbots and ticket routing
Option B

Text Classification

Assigns text to one of a fixed set of labels such as topic, language or category, without extracting parameters.

Strengths

  • Simple label output
  • Easy to evaluate
  • Good for reporting and tagging

An example

Message: Please cancel my subscription and refund my last payment.

  • A classifier might return billing.
  • Intent detection returns { "intent": "cancel_subscription", "confidence": 0.96, "entities": { "refund_requested": true } }, which your code can act on directly.

Routing on confidence

A common production pattern is to auto-route messages above a high confidence (for example 0.85), ask a clarifying question between 0.5 and 0.85, and send anything lower to a human or a general chat model. This keeps expensive generation calls for the messages that need them.

Frequently asked questions

Do I need training data for intent detection on RSFlowHub?
No. The endpoint uses a large language model, so it works from the message alone.
Is intent detection free on RSFlowHub?
It is part of the utility tier, so every plan includes a monthly quota of free intent detection requests.

Test it on your own data

Free plan with 100 credits/month + 1,000 bonus credits when you sign in before 23 Nov. One API key for every endpoint, with automatic provider fallback.

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