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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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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

Leveraging LangChain agents

LLMs are incredibly powerful, but they also have some fundamental flaws. We’ve discussed their limitations extensively; remember that they only have knowledge up to when their training data was cut off for ChatGPT, which was in late 2021. Surprisingly, technology this advanced is also not very good at other computational tasks, such as basic math, logic, or even the ability to look up other information that they don’t know about.

To compensate for these drawbacks, we need another technique or method to help out where our LLM needs assistance. These solutions are called agents, and, in this section, we’ll look at what they are, how they work, and how to use them with LangChain.

What is an agent?

Agents provide tooling for LLMs to carry out tasks that they can’t carry out themselves effectively. There are several different types of agents that are designed to work with different models, such as OpenAI LLMs, and provide...

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