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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

Core components of LangChain

LangChain offers a variety of modules that can be used to create language model applications. These modules can be used individually in simple applications or combined to create more complex ones. Flexibility is the key here!

The most common components of any LangChain chain are the following:

  • LLM model: LangChain’s core reasoning engine is your language model. LangChain makes it easy to use many different types of LLMs.
  • Prompt templates: These provide instructions to the LLM, controlling its output. Understanding how to construct prompts and different prompting strategies is crucial, so it’s good that we covered this in the previous chapter.
  • Output parsers: These convert the raw response from the LLM into a more usable format, simplifying downstream processing.

Let’s look at each of these core components in more detail.

Working with LLMs in LangChain

There are two different types of LLM models in LangChain...

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