A Gentle Introduction to Generative AI

It has become difficult to ignore the fact that Generative AI has become the most talked about technological advance in recent history. Chat applications such as ChatGPT and Bard have quickly put AI into the hands of the general public and almost every corporation is looking at ways to apply Generative AI to solve real business problems.

Hype aside, can Generative AI solve real problems? As a developer, you may be wondering how to implement Generative AI as a component in your projects. Can Large Language Models (LLMs) be used to answer real questions about my application's data?

In this example-driven session, we'll explore the essentials of Generative AI, including creating good prompts, providing context for prompts, binding completion responses to domain model objects, and enabling integration with custom data via functions and Retrieval Augmented Generation (RAG). Our exploration will touch on popular libraries for Python and Node, such as LangChain and LlamaIndex, but also touch on some of the newer options available to Java developers such as Spring AI and LangChain4J.

About Craig Walls

Craig Walls is a Principal Engineer, Java Champion, Alexa Champion, and the author of Spring AI in Action, Spring in Action, and Build Talking Apps. He's a zealous promoter of the Spring Framework, speaking frequently at local user groups and conferences and writing about Spring. When he's not slinging code, Craig is planning his next trip to Disney World or Disneyland and spending as much time as he can with his wife, two daughters, 1 bird and 2 dogs.

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