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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

By : Adrian Thompson
5 (3)
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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

5 (3)
By: Adrian Thompson

Overview of this book

ChatGPT for Conversational AI and Chatbots is a definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You’ll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you’ll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you’ll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. You’ll also cover popular use cases, such as using ChatGPT in conjunction with your own data. Later, the book focuses on creating a ChatGPT-powered chatbot that can comprehend and respond to queries directly from your unique data sources. The book then guides you through building chatbot UIs with ChatGPT API and some of the tools and best practices available. By the end of this book, you’ll be able to confidently leverage ChatGPT technologies to build conversational AI solutions.
Table of Contents (15 chapters)
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Part 1: Foundations of Conversational AI
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Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
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Part 3: Building and Enhancing ChatGPT-Powered Applications
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Index
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Working through a RAG example with LangChain

LangChain provides functionality to carry out all of the steps we’ve outlined. So, let’s look at a RAG example while looking at how we achieve the steps with LangChain in more detail.

For our use case, we’re going to look at using unstructured website data as the basis for our RAG system. This is a common example of a RAG application as most organizations have websites and unstructured data that they want to use. Imagine that your organization has asked you to create an LLM-powered chatbot that can answer questions about the content on your organization’s website.

In our scenario, we’ll explore leveraging unstructured website data as the foundation for our RAG system. Utilizing unstructured data from websites is a prevalent approach for RAG applications given that most organizations possess websites filled with data they wish to use. Imagine being tasked by your organization to develop a chatbot capable...

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