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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

Getting our data ready for the chatbot

To get our data ready, we first need to source and then process it so that it’s in the cleanest state, ready to be used.

Selecting our data sources

For this project, we’re going to need to provide a number of different types of context-specific knowledge to our LLM agent so that we can service the expected question types:

  • Hotel information: This would come from our online travel agent hotel dataset, so we’ll need some hotel data to represent the hotels we want to recommend to our users. The data we’re going to use to provide hotel recommendations is a small subset of the dataset at https://www.kaggle.com/datasets/raj713335/tbo-hotels-dataset. This dataset contains information on 1,000,000+ hotels from different countries and regions, such as their rates, reviews, amenities, location, and star ratings. The data was collected from various sources, such as hotel websites, online travel agencies, and review...

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