Genai on JAVAPRO International

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Tue, 30 Jun 2026 08:50:00 +0000

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Download free PDF here: Edition 06/2026 | Autonomous Java

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Tue, 28 Apr 2026 12:00:02 +0000

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Download free PDF here: Edition 04/2026 | Java in the Age of AI

Petabyte-Scale AI Memory with Serverless Java

Wed, 15 Apr 2026 07:00:01 +0000

The current wave of Generative AI innovation is built on a paradox. While models are becoming more powerful and efficient, the infrastructure required to operate them at scale is becoming increasingly wasteful. Enterprises are investing millions into compute, storage, and energy, yet a significant portion of these resources remains idle. Modern vector databases, graph systems, and caching layers are based on a monolithic architecture run as always-on clusters, consuming CPU and RAM regardless of whether data is actively accessed. Studies and industry analyses consistently show that up to 80% of compute resources in such systems are effectively wasted on idle workloads.

High-Performance Vector-Search Grids with Java

Wed, 08 Apr 2026 07:00:02 +0000

The rapid rise of Generative AI has fundamentally changed how modern systems are designed. While much of the attention has focused on large language models and inference pipelines, the real differentiator in production systems lies elsewhere: in the infrastructure that delivers context to those models. Retrieval-Augmented Generation (RAG) has become the de facto pattern for enterprise GenAI. It relies on fast, scalable access to embeddings, metadata, and relationships. Yet for Java developers, building such systems at scale has remained a challenge. External vector databases introduce latency, fragmentation, and operational complexity. Distributed caches struggle with memory inefficiency and serialization overhead. Traditional databases cannot handle high-dimensional similarity search at the required speed.

Caching and Beyond: Smarter Data Processing with Java - in 2 Hours

Thu, 02 Apr 2026 11:45:01 +0000

Many Java teams start simple — a single database, a straightforward architecture. But as systems evolve, so does the stack: caching layers to improve performance, search engines for faster queries, additional databases for specialized use cases, and now vector stores for AI-driven features.

What begins as optimization often turns into a fragmented landscape of technologies. The result: increasing complexity, higher infrastructure costs, and growing DevOps overhead.

The session “Caching and Beyond – Smarter Data Processing with Java” at JCON EUROPE takes a different approach. Instead of adding more tools, it focuses on simplifying data architectures — using a clean, Java-centric model built on open-source technologies.

Build Vector Database Apps with Pure Java

Thu, 02 Apr 2026 07:00:01 +0000

The Java ecosystem is entering a new phase. Generative AI is no longer experimental; it is becoming a foundational capability in enterprise systems. Yet while frameworks for inference and orchestration have matured, one critical bottleneck continues to slow down adoption: data access for semantic retrieval.

At the heart of modern GenAI systems lies Retrieval-Augmented Generation (RAG). This paradigm depends on fast access to embeddings - high-dimensional vectors that represent meaning. For Java developers, integrating this capability has historically meant introducing an external vector database into the architecture. What appears to be a straightforward design decision quickly becomes a source of complexity, inefficiency, and architectural friction.

More Than Just a Conference for Java Enthusiasts | JCON 2026

Tue, 24 Mar 2026 12:00:02 +0000

With over 100 speakers, more than 100 sessions, and a diverse supporting program, JCON Europe 2026 once again positions itself as one of the leading events for the Java community. Taking place in Cologne as an in-person conference, the event combines deep technical expertise with a strong focus on creating a highly customizable attendee experience.

A Conference Tailored to You

JCON Europe 2026 is designed to give participants full control over their conference journey. A wide variety of session formats, thematic tracks, and networking opportunities allow developers to build a schedule that aligns with their interests, experience level, and career goals.

High-Demand Workshops at JCON EUROPE 2026 – Tickets Available Now

Mon, 16 Mar 2026 12:00:01 +0000

Cologne – JCON EUROPE returns to its venue from April 20-23, once again offering an extensive program for Java enthusiasts. In addition to over 100 speakers and sessions, Monday, April 20, is fully dedicated to hands-on, expert-led workshops.

Each workshop is a two-hour deep dive into cutting-edge technologies and real-world development topics. Participants can choose from a total of seven different sessions across two time slots: 1:00–3:00 PM and 4:00–6:00 PM. A coffee break in between provides room for informal networking.

Can GenAI Help Reduce Energy Used by Java Code?

Fri, 13 Feb 2026 07:00:02 +0000

Some recent studies show that IT is emitting more CO2 than civil aviation! As software developers, there is something we can do: optimising our code. Making our code run more efficiently should reduce the carbon footprint of IT. It will consume less CPU, RAM, disk, and network. Hardware being powered by electricity, these optimisations should reduce our electricity bills and lower emissions due to generation of that electricity. Optimised software also extends hardware lifespan by reducing the need for upgrades, diminishing the embodied carbon from manufacturing and avoiding raw material extraction (e.g., rare earths mining).

JCON Meets Enterprise AI - From Basics to Production

Thu, 29 Jan 2026 12:00:01 +0000

From April 20–23, 2026, the European Java community will once again gather in Cologne for JCON EUROPE 2026. Taking place at the Cinedom multiplex cinema, the conference brings together developers, architects, and Java enthusiasts for four days of knowledge sharing, inspiration, and real-world experience — all on the big screen.

Under the motto “Big screen. Big community. Big AI.”, JCON EUROPE 2026 offers an in-person program running daily from 9:00 to 18:00 (CEST), combining technical depth with a strong community focus.

Building MCP Tools (for AI Agents) using Spring AI

Wed, 07 Jan 2026 07:00:02 +0000

Introduction

The innovation of AI Agents and Agentic AI systems has revolutionized the adoption of Generative AI. Agents empower systems to autonomously achieve goals by performing tasks. Tools are the key building blocks for AI agents that provide them with superior decision-making and implementation capabilities. Model Context Protocol (MCP) protocol has standardized the approach of building tools enabling faster, seamless integration. In this paper, we are going to talk about MCP, the underlying architecture. We will also provide a simple code example of building MCP tools in Java using Spring AI and integrating it with an AI Agent.

Solutions to achieve desired results from Generative AI Models

Wed, 10 Dec 2025 07:00:03 +0000

Rise of Generative AI

Generative AI (GenAI) has gained significant prominence in the last couple of years. This technological breakthrough has created the possibility of integrating various innovative use cases into applications across domains. Organizations of all sizes, small and large, have started exploring, experimenting and adopting GenAI. Tools, frameworks and platforms are rapidly evolving and allow application developers to easily integrate GenAI capabilities into their existing applications or to build new innovative applications leveraging GenAI. There are lots of options available – GenAI capabilities available as services, foundation models, open-source models and model provider platforms that allow hosting of models.

Early Bird 30% — JCON GenAI — The Enterprise Java AI Revolution Begins

Mon, 20 Oct 2025 07:00:03 +0000

No more Python.
The moment for enterprise-grade Generative AI in Java has arrived — and you have the advantage! Almost all major enterprise systems, and the mountains of proprietary data they hold, are built on Java. That makes Java the perfect foundation for high-impact AI projects — but only if you know how to build them right.

JCON GenAI is your high-octane, single-day hands-on workshop series, designed to get you out of theory and into production-ready code. Join leading experts from the Java community and master the tools, architectures, and security foundations needed to drive your company’s AI transformation.

Code Reviews with AI: a Developer Guide

Wed, 30 Jul 2025 07:00:01 +0000

Code reviews are a cornerstone of software development. They’re where we share knowledge, catch bugs early, and ensure our code meets the highest standards.

But let’s be honest…

Traditional code reviews can be time-consuming and tedious and sometimes even miss subtle yet critical issues. Enter the age of AI-powered code review, a game-changer that addresses these challenges and elevates code quality to new heights.

This article dives into the common pitfalls of code reviews and explores how AI tools can revolutionize each phase of the development lifecycle.

I will discuss the use and impact of AI on the different phases of the SDLC from the 2 main perspectives of the developer and the reviewer. I will also talk specifically about the use of AI in the Code Review and how to implement it productively. I will provide examples of tools used in the different phases: Github Copilot, SonarQube, Qodo, and IntelliJ.

Evaluating RAG Pipelines with the RAG Triad

Wed, 14 May 2025 07:00:03 +0000

Retrieval-Augmented Generation (RAG) emerged as a dominant framework for feeding Large Language Models (LLMs) the context beyond the scope of their training data and enabling LLMs to respond with more grounded answers and fewer hallucinations based on that context.

However, designing an effective RAG pipeline can be challenging. You need to answer questions such as:

How should you parse and chunk text documents for vector embedding? What chunk size and overlay size should you use?
What vector embedding model should you use?
What retrieval method should I use to fetch the relevant context? How many documents should you retrieve by default? Does the retriever actually manage to retrieve the applicable documents?
Does the generator actually generate content that is in line with the retrieved context? What parameters (model, prompt template, temperature) work better?

The only way to objectively answer these questions is to measure how well the RAG pipeline works, but what exactly do you measure, and how do you measure it? This is the topic I’ll cover here.

Vector Embedding in Java Applications: Gen-AI Functions Directly from the Database

Tue, 06 May 2025 11:30:01 +0000

Generative AI (GenAI) has rapidly advanced in recent years and is increasingly finding its way into practical software development. What was once considered a research topic is now becoming a tool for everyday developers — including in the Java space. Vector embeddings play a key role here: they enable functions like semantic search, text classification, or recommendation systems, opening up entirely new application possibilities.

But how can these technologies be integrated into existing Java applications without the need for complex frameworks or external cloud services?