Part 3: Building and Enhancing ChatGPT-Powered Applications

In this part, we will explore Retrieval-Augmented Generation (RAG) systems, demonstrating how to use vector stores as a knowledge base. Then, we will bring together all the concepts covered so far to build and enhance ChatGPT-powered applications. You’ll work on your own practical project to create a chatbot that can handle complex tasks and provide personalized interactions. Finally, we will look into the future of conversational AI and large language models (LLMs), discussing the challenges of taking ChatGPT applications to production and examining alternative technologies and emerging trends in the field.

This part has the following chapters:

• Chapter 7, Vector Stores as Knowledge Bases for Retrieval-augmented Generation
• Chapter 8, Creating Your Own LangChain Chatbot Example
• Chapter 9, The Future of Conversational AI with LLMs