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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
4
Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
9
Part 3: Building and Enhancing ChatGPT-Powered Applications

Summary

In this chapter, we looked at a real-world example of creating a ChatGPT-powered conversational agent, using some of the techniques and technologies that we covered in earlier chapters. Our example was thoughtfully designed to cover the use cases, functionality, and features of our chatbot, allowing us to apply our knowledge of LangChain, RAG systems, and related tools effectively. You were also introduced to the Streamlit library to create rich chat interfaces and saw how to use this with LangChain. Hopefully, we’ve implemented our code base to make our agent extensible and something that we could use for a more complex project. We’ve achieved our aim of creating a ChatGPT conversational agent with knowledge of our own data and the capability to use external information and sources.

We’ve created our POC and validated our concept. It’s a start, but there is a lot to consider before creating a more extensive application and going live. In our...

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