• Author(s): Loaine Groner
  • Medium: InfoQ Dev Summit Boston 2025

Introduction: What’s This All About? 🤔

Building AI-powered applications can feel overwhelming, especially when you’re working with Java. But what if there was a way to simplify the process and leverage the power of Large Language Models (LLMs) without a ton of extra effort? This blog post dives into a presentation on Spring AI, a framework designed to do just that, and explores how to tackle a common challenge: connecting your database fields to the language your AI understands.

Chapter 1: The Core Problem Being Solved 🎯

Many businesses want to use AI to automate tasks, answer questions, or provide personalized experiences. However, integrating AI, particularly LLMs, into existing Java applications can be complex. A major hurdle is often connecting the structured data stored in databases to the unstructured language that LLMs work with. The presentation highlighted this challenge and introduced a framework to make the process much easier.

Chapter 2: Introducing Spring AI 💡

Spring AI is a framework built on top of the popular Spring ecosystem, designed to simplify the integration of LLMs into Java applications. It focuses on a technique called Retrieval-Augmented Generation (RAG). Let’s break down what that means:

  • LLM (Large Language Model): Think of this as the “brain” of your AI – a powerful model trained on massive amounts of text data.
  • RAG (Retrieval-Augmented Generation): Instead of relying solely on its internal knowledge, the LLM retrieves relevant information from a database (or other data source) and uses that information to generate a more accurate and contextually relevant response. This is like giving the LLM a cheat sheet!
  • Spring AI: This framework provides the tools and structure to build RAG-powered applications in Java. It handles many of the technical details, allowing developers to focus on the logic of their AI solutions.

Chapter 3: How It Works: A Technical Deep Dive ⚙️

Let’s explore how Spring AI helps you build these AI solutions. Here’s a breakdown of the key components and how they work together:

  • Advisors: These act as “interceptors” that can modify prompts, sanitize data, or even block certain actions. They give you fine-grained control over the AI’s behavior.
  • Tools: Think of these as functions or methods that the LLM can call. They’ve been defined as Spring beans, making them easy to manage. The LLM decides when to use these tools based on the prompt and the context.
  • Agentic AI vs. Workflows: The presentation distinguished between predefined sequences of actions (“workflows”) and more autonomous AI (“agentic AI”), where the LLM has more freedom in choosing which tools to use.
  • The Database Field Mapping Challenge: A common issue arises when your database fields have names that don’t directly translate to understandable language. For example, a field named “ABC” might represent a “booking ID.” Here’s how to tackle this:

    1. Prompt Engineering is Key: The most important thing is to explicitly tell the LLM how to interpret the data. Use clear instructions in your prompts. For example:

      "You are a travel agent.  You will receive information about a booking.  The booking information is provided in the following format:
- ABC: This represents the booking ID.
- DEF: This represents the first name.
- GHI: This represents the last name.

The booking ID is: [ABC value]
The first name is: [DEF value]
The last name is: [GHI value]

Please confirm the booking details before proceeding."

    2. Data Transformation (Optional): While less desirable, you could rename fields before feeding data to the LLM.

    3. Metadata/Contextual Information: Provide extra information alongside the data to help the LLM understand the fields.

    4. Few-Shot Learning (Advanced): Provide examples of how to interpret the data.

Chapter 4: Key Takeaways & Actionable Insights 📋

Here’s a quick reference guide to the most important lessons from the presentation:

  • ✅ Spring AI simplifies AI integration: It makes building AI-powered Java applications much easier.
  • 💡 RAG is your friend: Leverage Retrieval-Augmented Generation to provide context to your LLMs.
  • ✍️ Prompt engineering is crucial: Well-crafted prompts are essential for accurate and reliable AI results.
  • 🛠️ Control with Advisors: Use Advisors to customize and control the AI’s behavior.
  • 🧪 Testing is evolving: Testing AI solutions within Spring AI is still developing, but it’s an area of active improvement.
  • ✨ Embabel is coming: Keep an eye out for Embabel, a new agent framework built on Spring AI.

Conclusion

Spring AI represents a significant step forward in making AI accessible to Java developers. While challenges remain, particularly in testing and advanced prompt engineering, the framework provides a solid foundation for building powerful and intelligent applications. As AI continues to evolve, tools like Spring AI will be essential for unlocking its full potential. 🚀

Appendix