Many of our customers have utilized the Intent feedback analyzer to continuously improve their NLU models with tremendous success. However, at Cognigy, we are regularly asked about a more systematic approach to Intent training in projects.

So, we created a comprehenisve tutorial to present a systematic technique that can be employed during the project planning process.

Visit our Help Center Article to delve deep into a systematic approach to Intent training.

 

In the article, you'll learn about the phases that can be used to outline the intent creation/training process:

Introduction

Improving Intent recognition

  • Machine learning

  • Cognigy Script

Resolving Intent conflicts

  • Moving Example Sentences to the better-suited Intent

  • Moving Intents into a hierarchy

  • Merging multiple Intents into one

  • Outsourcing Intents to a separate Flow

  • Adjusting the NLU settings

Reducing false positives

  • Machine learning / Reject Intent

  • Adjusting the NLU settings

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