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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

By : Adrian Thompson
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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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1
Part 1: Foundations of Conversational AI
4
Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
9
Part 3: Building and Enhancing ChatGPT-Powered Applications

Vector Stores as Knowledge Bases for Retrieval-augmented Generation

Retrieval-augmented generation (RAG) is easily the most common use case for LLMs that has emerged since the explosion of ChatGPT. In this chapter, we’re going to look at the key steps and concepts involved in creating a RAG system. Once you have an understanding of what’s involved with each step, we’ll look at how these processes and techniques can be carried out using LangChain. Going further, we’ll work through our own RAG system with a real-world example.

This chapter aims to be an introduction to the core concepts of RAG so that you have a solid base for mastering it.

In this chapter, we’ll cover the following topics:

  • Why do we need RAG?
  • Understanding the steps needed to create a RAG system
  • Working through a RAG example with LangChain

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