Craig Walls

Building Intelligent Spring Applications with Spring AI

9:00 AM MDT

By now, you've no doubt noticed that Generative AI is making waves across many industries. In between all of the hype and doubt, there are several use cases for Generative AI in many software projects. Whether it be as simple as building a live chat to help your users or using AI to analyze data and provide recommendations, Generative AI is becoming a key piece of software architecture.

So how can you implement Generative AI in your projects? Let me introduce you to Spring AI.

For over two decades, the Spring Framework and its immense portfolio of projects has been making complex problems easy for Java developers. And now with the new Spring AI project, adding Generative AI to your Spring Boot projects couldn't be easier! Spring AI brings an AI client and templated prompting that handles all of the ceremony necessary to communicate with common AI APIs (such as OpenAI and Azure OpenAI). And with Spring Boot autoconfiguration, you'll be able to get straight to the point of asking questions and getting answers your application needs.

In this handson workshop, you'll build a complete Spring AIenabled application applying such techniques as prompt templating, Retrieval Augmented Generation (RAG), conversational history, and tools invocation. You'll also learn prompt engineering techniques that can help your application get the best results with minimal “hallucinations” while minimizing cost.

What's New in Spring and Spring Boot

8:30 AM MDT

In this example-driven session, we'll review several tips and tricks to make the most out of your Spring development experience. You'll see how to apply the best features of Spring and Spring Boot, including the latest and greatest features of Spring Framework 6.x and Spring Boot 3.x with an eye to what's coming in Spring 7 and Boot 4.

Spring has been the de facto standard framework for Java development for nearly two decades. Over the years, Spring has continued to evolve and adapt to meet the ever-changing requirements of software development. And for nearly half that time, Spring Boot has carried Spring forward, capturing some of the best Spring patterns as auto-configuration.

As with any framework or language that has this much history and power, there are just as many ways to get it right as there are to get it wrong. How do you know that you are applying Spring in the best way in your application?

Spring AI in Action

10:30 AM MDT

By now, you've no doubt noticed that Generative AI is making waves across many industries. In between all of the hype and doubt, there are several use cases for Generative AI in many software projects. Whether it be as simple as building a live chat to help your users or using AI to analyze data and provide recommendations, Generative AI is becoming a key piece of software architecture.

So how can you implement Generative AI in your projects? Let me introduce you to Spring AI.

For over two decades, the Spring Framework and its immense portfolio of projects has been making complex problems easy for Java developers. And now with the new Spring AI project, adding Generative AI to your Spring Boot projects couldn't be easier! Spring AI brings an AI client and templated prompting that handles all of the ceremony necessary to communicate with common AI APIs (such as OpenAI and Azure OpenAI). And with Spring Boot auto-configuration, you'll be able to get straight to the point of asking questions and getting answers your application needs.

In this session, we'll consider a handful of use cases for Generative AI and see how to implement them with Spring AI. We'll start simple, then build up to some more advanced uses of Spring AI that employ your application's own data when generating answers.

Observing AI with Spring Boot and Spring AI

1:00 PM MDT

Modern application observability involves tracking key metrics and tracing the flow of an application, even across service boundaries. Spring Boot 3 introduced some powerful metrics and tracing capabilities based on Micrometer to open a window into your application's inner-workings.

Among the things you might want to keep an eye on in your Generative AI applications are how many interactions and how much time is spent with vector stores and AI provider APIs and, of course, how many tokens are being spent by your application. And being able to trace the flow of prompts, data, and responses through your application can help identify problems and bottlenecks.

Great news! Spring AI comes equipped to record metrics and tracing information through Micrometer. In this session, you'll learn how to put Spring AI observability to work for you. You'll learn about the metrics it exposes as well as the keys you can use to build dashboards and tracing to build a window into your Generative AI applications.

Spring Graph QL

3:00 PM MDT

In this example-driven session, we're going to look at how to implement GraphQL in Spring. You'll learn how Spring for GraphQL builds upon GraphQL Java, recognize the use-cases that are best suited for GraphQL, and how to build a GraphQL API in Spring.

Typical REST APIs deal in resources. This is fine for many use cases, but it tends to be more rigid and less efficient in others.

For example, in an shopping API, it's important to weigh how much or how little information should be provided in a request for an order resource? Should the order resource contain only order specifics, but no details about the order's line items or the products in those line items? If all relevant details is included in the response, then it's breaking the boundaries of what the resource should offer and is overkill for clients that do not need it. On the other hand, proper factoring of the resource will require that the client make multiple requests to the API to fetch relevant information that they may need.

GraphQL offers a more flexible alternative to REST, setting aside the resource-oriented model and focusing more on what a client needs. Much as how SQL allows for data from multiple tables to be selected and joined in response to a query, GraphQL offers API clients the possibility of tailoring the response to provide all of the information needed and nothing that they do not need.

(Re)introducing Spring Security

5:00 PM MDT

Spring Security has long been a powerful guard to place around your Spring applications, providing authentication, authorization, and many more concerns around keeping your application secure.

As time has progressed, Spring Security has evolved to provide even more capabilities, but has applied some self-improvement to make working with Spring Security even easier. That is to say, the way you configure and apply Spring Security today has changed dramatically from it's early XML-oriented approach and is even different now than some of the more recent Java-based configuration strategies.

In this example-driven session, we'll explore the latest and greatest that Spring Security has to offer, with an emphasis on how to apply security aspects to your applications with the latest configuration styles supported by Spring Security. You'll see how security is enabled in modern Spring applications using the Lambda DSL configuration approach, the preferred way to configure Spring Security and the ONLY way to configure Spring Security 7.

Introducing Spring Modulith

9:00 AM MDT

Introducing Spring Modulith

Although microservices are still a useful architectural choice, the balance of additional complexity and the advantages of microservice architecture do not necessarily work out in the benefit of all applications. While most application will benefit from improved modularity, the challenges that come with distributed computing may be too much for some applications to take on. A well-structured and modular monolithic application might be a better fit.

In this session, we'll explore Spring Modulith, a relatively new Spring library that enables developers to build well-structured Spring Boot applications, guiding them in discovering domain-driven modules, and verifying that the modular arrangement is correct. We'll also see how Spring Modulith assists with modular integration testing and documentation.

Penning Powerful Prompts: Crafting effective prompts to get the best from an LLM

11:00 AM MDT

In this session, we'll cover several useful prompt engineering techniques as well as some emerging patterns that are categorized within the “Agentic AI” space and see how to go beyond simple Q&A to turn your LLM of choice into a powerful ally in achieving your goals.

At it's core, Generative AI is about submitting a prompt to an LLM-backed API and getting some response back. But within that interaction there is a lot of nuance, particularly with regard to the prompt itself.

It's important to know how to write effective prompts, choosing the right wording and being clear about your expectations, to get the best responses from an LLM. This is often called “prompt engineering” and includes several patterns and techniques that have emerged in the Gen AI space.