Photo credit Migrating an application always feels a bit daunting, and that was the way I felt when I looked at the Spring AI 1.0 milestone release. However, the upgrade didn’t make as many major shifts as anticipated. This guide will help you upgrade your Spring AI application from version 0.8 to 1.0 and explain why each change is needed. Changes include some naming shifts, configuration options, and more.

Photo credit There are so many options when it comes to languages, frameworks, and tools for building generative AI (GenAI) applications. When you are just getting started, these decisions and figuring out how to integrate everything can be overwhelming. My team has been working on some pre-packaged solutions to make this process easier by providing starter kit projects with a few key technologies. One of those is the topic of today’s post - building a GenAI application with Spring AI in Java.

Photo credit Generative AI (GenAI) is currently a hot topic in the tech world. It’s a subset of artificial intelligence that focuses on creating new content, such as text, images, or music. One popular type of GenAI component is the Large Language Model (LLM), which can generate human-like text based on a prompt. Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of generative AI models by grounding them in external knowledge sources.

Photo credit What is RAG? This term is used a lot in today’s technical landscape, but what does it actually mean? Here are a few definitions from various sources: "Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response." - Amazon Web Services "Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.