Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
ChatGPT for Conversational AI and Chatbots

You're reading from   ChatGPT for Conversational AI and Chatbots Learn how to automate conversations with the latest large language model technologies

Arrow left icon
Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781805129530
Length 250 pages
Edition 1st Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
Adrian Thompson Adrian Thompson
Author Profile Icon Adrian Thompson
Adrian Thompson
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Part 1: Foundations of Conversational AI FREE CHAPTER
2. Chapter 1: An Introduction to Chatbots, Conversational AI, and ChatGPT 3. Chapter 2: Using ChatGPT with Conversation Design 4. Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
5. Chapter 3: ChatGPT Mastery – Unlocking Its Full Potential 6. Chapter 4: Prompt Engineering with ChatGPT 7. Chapter 5: Getting Started with LangChain 8. Chapter 6: Advanced Debugging, Monitoring, and Retrieval with LangChain 9. Part 3: Building and Enhancing ChatGPT-Powered Applications
10. Chapter 7: Vector Stores as Knowledge Bases for Retrieval-augmented Generation 11. Chapter 8: Creating Your Own LangChain Chatbot Example 12. Chapter 9: The Future of Conversational AI with LLMs 13. Index 14. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Make sure you have installed LangSmith by running pip install -U langsmith.”

A block of code is set as follows:

def format_evaluator_inputs(run: Run, example: Example):
    return {
        "input": example.inputs["question"],
        "prediction": next(iter(run.outputs.values())),
        "reference": example.outputs["answer"],
    }

Any command-line input or output is written as follows:

splitter = RecursiveCharacterTextSplitter(chunk_size=250, 
    chunk_overlap=20)

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Clicking on the SUCCESS or FAILURE button will display details of the test for each input and output in the dataset.”

Tips or important notes

Appear like this.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image