Effective Conversational AI

Create and improve conversational AI with the latest patterns, best practices, and tools, including generative AI models.

Conversational AI (CAI) tools are built to solve problems, but all-too-often they just end up causing pain for users–and developers! Effective Conversational AI reveals best practices and industry-tested techniques for creating chatbots and conversational AI tools that are reliable at an enterprise scale. With the tested ideas and examples in this book, you’ll learn to build chatbots that your customers and colleagues will actually want to use!

In Effective Conversational AI you’ll learn how to:

• Create high-quality chatbots and other conversational AI experiences
• Plan for continuous improvement
• Incorporate generative AI solutions to improve quality, accuracy, and usability
• Evaluate user experience and business results

Effective Conversational AI introduces continuous improvement practices that are vital for the constant betterment and evolution of chatbots and CAI tools. It introduces the three most-common forms of chatbot—Q&A, process-oriented, and routing agents—and presents a reliable framework for continuously improving each one. Using modern generative AI and tried-and-tested classic approaches, you’ll learn to deliver high performance chatbots that can guide a customer through complex end-to-end tasks—no human required!

Foreword by Jesús Mantas.

About the technology

Powerful new chatbot frameworks and Generative AI models can practically eliminate problems like misinterpreting user intent and delivering nonsensical answers. In this book, you’ll learn how to build chatbots that take advantage of large language models and other modern tools and create conversational AI experiences users will love.

About the book

Effective Conversational AI teaches you how to build great chatbots that perform reliably even at enterprise scale. In it, you’ll learn how to clarify user intent using LLMs, respond accurately to unanticipated input, and use Retrieval Augmented Generation to keep responses up to date. Along the way, you’ll discover how to establish a feedback loop for continuous quality improvement and master techniques to integrate GenAI safely into conventional chatbot designs.

What's inside

• Blend Generative AI and conventional chatbot tools
• Use LLMs to improve quality, accuracy, and usability
• Plan for continuous improvement
• Domain-specific responses using RAG

About the reader

For developers, engineers, and product managers working with conversational AI.

About the author

Andrew Freed, Cari Jacobs, and Eniko Rózsa are seasoned conversational AI developers with IBM.

Table of Contents

Part 1
1 What makes conversational AI work?
2 Building a conversational AI
3 Planning for improvement
Part 2
4 Understanding what your users really want
5 Improving weak understanding for traditional AI
6 Enhancing responses with retrieval-augmented generation
7 Augmenting intent data with generative AI
Part 3
8 Streamlining complex flows
9 Harnessing context for an adaptive virtual assistant experience
10 Reducing complexity with generative AI
Part 4
11 Reducing opt-outs
12 Conversational summarization for smooth handoff

Andrew Freed is a Distinguished Engineer and a Conversational AI delivery architect who has successfully delivered multi-million-dollar CAI contracts. He is the author of Conversational AI (Manning).

Eniko Rozsa is a Distinguished Engineer and leads a Conversational AI delivery practice and is chief architect of a “Conversational Insights” tool used to analyze and improve Conversational AI. She has invented and designed her first Conversational AI solution in 2008, before it was productized and delivered to Fortune 500 clients.

Cari Jacobs is an expert Conversational AI delivery specialist with a series of articles on Medium.com (https://medium.com/@heyitscari). She is a delivery practice lead and designs, implements, and improves conversational AI solutions for Fortune 500 clients.