ChatGPT for Conversational AI and Chatbots

Name: ChatGPT for Conversational AI and Chatbots | Learn how to automate conversations with the latest large language model technologies
Brand: Packt Publishing Limited
Price: 20.99 EUR
Availability: OnlineOnly

Learn how to automate conversations with the latest large language model technologies

Adrian Thompson(Author)

Packt Publishing Limited

1st Edition

Published on 18. March 2025

250 pages

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978-1-80512-235-7 (ISBN)

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Content

Cover
Title Page
Copyright and credits
Contributors
Table of Contents
Preface
Part 1: Foundations of Conversational AI
Chapter 1: An Introduction to Chatbots, Conversational AI, and ChatGPT
What are chatbots and conversational AI?
A brief history of conversational AI
An overview of chatbots and automated assistants
Evolution of chatbots and conversational AI
Understanding conversational AI applications
Customer service
Language translation
Education
Healthcare
Banking and insurance
Retail
Human resources
Conversational AI as a training tool - the emergence of digital humans
Conclusion
What is OpenAI's ChatGPT?
Understanding the large language technology behind ChatGPT
The role of tokens in LMs
Understanding OpenAI's LMs
Conclusion
Capabilities and applications of ChatGPT
Capabilities of ChatGPT
How smart is ChatGPT?
Applications of ChatGPT
Limitations of ChatGPT
Limitations
Risks and security
Summary
Further reading
Chapter 2: Using ChatGPT with Conversation Design
Technical requirements
Understanding conversation design
Exploring the role of conversation designers
Working with practical applications of ChatGPT in conversation design
Intent clustering with ChatGPT
Understanding utterance and entity generation
Using ChatGPT to help write your dialogue
Persona creation with ChatGPT
Using ChatGPT for user research
Creating our user and chatbot personas
Simulating conversations
What is a sample dialogue?
Creating sample dialogue
Creating sample dialogue with personas
Testing and iteration in conversation design with ChatGPT
Testing with ChatGPT
Summary
Further reading
Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
Chapter 3: ChatGPT Mastery - Unlocking Its Full Potential
Technical requirements
Mastering the ChatGPT interface
The Free and Plus versions of ChatGPT
ChatGPT interface
GPTs
Exploring OpenAI Playground
Getting started
UI features
Pricing for API and Playground
Learning to use the ChatGPT API
Getting started
Calling the API directly
Setting up with the OpenAI Python library
Setting up with the OpenAI Node.js library
Other ChatGPT libraries
Summary
Further reading
Chapter 4: Prompt Engineering with ChatGPT
Technical requirements
Going through the concepts of prompt engineering
Understanding the core components of a successful prompt
Instruction
Context
Scope
Role
Audience
Input data
Output data
Conclusion
Working with a prompt engineering strategy
Define clear goals
Employ iterative prompt development
Start simple
Use follow-up prompts to test against multiple examples
Use temperature when you need to
Handling memory limitations in ChatGPT
Conclusion
Knowing the prompt engineering techniques
Few-shot learning for a customer support chatbot
Prompting to summarize data for a conversational agent
Prompting to create your own chatbot powered by ChatGPT
Summary
Further reading
Chapter 5: Getting Started with LangChain
Technical requirements
Introduction to LangChain
LangChain libraries
Core components of LangChain
Working with LLMs in LangChain
Prompt templates
Using output parsers
Understanding LangChain Expression Language
What is LCEL?
Key components of LCEL
Runnable protocol
A simple example of LCEL
Creating different LangChain chains
Basic chain example
Creating a sequential chain to investigate conversational data
Utilizing parallel chains in LangChain for efficient multi-source information gathering
Routing chains to answer questions effectively
Summary
Further reading
Chapter 6: Advanced Debugging, Monitoring, and Retrieval with LangChain
Technical requirements
Debugging and monitoring LangChain
Understanding tracing techniques
Introducing LangSmith
Leveraging LangChain agents
What is an agent?
What are LangChain tools?
An introduction to OpenAI tool calling
Plug-and-play LangChain tools for immediate integration
An out-of-the-box tool example
Using our tool with an agent
Creating a custom weather tool
Exploring LangChain memory
Exploring the different types of memory applications
Understanding memory challenges
Introducing memory usage techniques
Understanding an example of using memory in LangChain
Summary
Further reading
Part 3: Building and Enhancing ChatGPT-Powered Applications
Chapter 7: Vector Stores as Knowledge Bases for Retrieval-augmented Generation
Technical requirements
Why do we need RAG?
Understanding the steps needed to create a RAG system
Defining your RAG data sources
Preprocessing our content and generating embeddings
Chunking for effective LLM interactions
Creating embeddings with OpenAI models
Storing and searching our embeddings with a vector store
Deciding which vector database to use
Working through a RAG example with LangChain
Integrating data - choosing your document loader
Creating manageable chunks with text splitting
Creating and storing text embeddings
Bringing everything together with LangChain
Summary
Further reading
Chapter 8: Creating Your Own LangChain Chatbot Example
Technical requirements
Scoping our ChatGPT project
A holiday assistant use case
A persona outline for Ellie the explorer
Ellie's conversational scope
Technical features
Getting our data ready for the chatbot
Selecting our data sources
Preparing the hotel data
Creating our agent for complex interactions
Creating the agent tools
Bringing it all together - building Your own LangChain Chatbot with Streamlit
Creating secrets and config management
Creating our agent service
Building our Streamlit chat app
Running and testing Ellie, our chatbot application
Ways to improve Ellie
Summary
Further reading
Chapter 9: The Future of Conversational AI with LLMs
Technical requirements
Going into production
Understanding the dangers of going into production
Challenges of RAG systems
Evaluating production systems
Components of an evaluation system
Learning how to use LangSmith to evaluate your project
Application monitoring in production with LangSmith
Advanced monitoring features with LangSmith
Tracing and data management
Monitoring tools
Advanced monitoring features
Alternatives to ChatGPT and LangChain
Some alternatives to ChatGPT and OpenAI LLMs
Some alternatives to LangChain
Looking at the growing LLM landscape
The growth of the small language model (SLM)
Are LLMs reaching their limit?
Enter SLMs
SLMs versus LLMs - key differences
Advantages of SLMs
Disadvantages of SLMs
Some examples of SLMs
The transformative potential of SLMs
Where to go from here
Summary
Further reading
Index
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ChatGPT for Conversational AI and Chatbots

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