How do we improve intent recognition accuracy?
Fine-tuned intent models boost conversion rates.
How It Works:
Augment training with diverse examples, apply contextual embeddings, and use active learning to surface ambiguous utterances for manual labeling.
Key Benefits:
- Higher precision: Fewer false positives.
- Adaptive models: Learn from live data.
- Better UX: Smooth user journeys.
Real-World Use Cases:
- Support bots: Reduce fallbacks to human agents.
- Survey analysis: Automatically tag open-ended responses.
FAQs
What embedding models to use?
How monitor drift?
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