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Conversational AI with Rasa

You're reading from   Conversational AI with Rasa Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077057
Length 264 pages
Edition 1st Edition
Tools
Concepts
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Authors (2):
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Xiaoquan Kong Xiaoquan Kong
Author Profile Icon Xiaoquan Kong
Xiaoquan Kong
Guan Wang Guan Wang
Author Profile Icon Guan Wang
Guan Wang
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: The Rasa Framework
2. Chapter 1: Introduction to Chatbots and the Rasa Framework FREE CHAPTER 3. Chapter 2: Natural Language Understanding in Rasa 4. Chapter 3: Rasa Core 5. Section 2: Rasa in Action
6. Chapter 4: Handling Business Logic 7. Chapter 5: Working with Response Selector to Handle Chitchat and FAQs 8. Chapter 6: Knowledge Base Actions to Handle Question Answering 9. Chapter 7: Entity Roles and Groups for Complex Named Entity Recognition 10. Chapter 8: Working Principles and Customization of Rasa 11. Section 3: Best Practices
12. Chapter 9: Testing and Production Deployment 13. Chapter 10: Conversation-Driven Development and Interactive Learning 14. Chapter 11: Debugging, Optimization, and Community Ecosystem 15. Other Books You May Enjoy

Introduction to CDD

CDD is a methodology that enables you to develop a dialogue system; it was introduced by the Rasa team. It is an iterative and interactive process: developers observe the behavior of users and improve chatbot performance based on those observations.

CDD involves the following steps:

  1. Sharing your bot: We should distribute our product prototype for user testing as soon as possible. No matter how hard developers try, users will always have something new to input into the chatbot. Many teams spend months developing chatbots and focusing on conversations that, in reality, users never have.
  2. Reviewing conversations: We should spend time studying the conversation between users and our chatbot. It is very helpful to study real user conversations at each stage of development (from the prototype to the real product). Far too many teams only focus on simple attributes, such as how many users express certain intentions and so on. Instead, they should spend more...
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