Markus Eisele’s Post

Exploring how Java + AI can shape the future of enterprise applications. My key takeaway: AI isn’t replacing Java — it’s making applications smarter. Sharing a simple visual that captures this thinking 👇 Curious how others see this space evolving. #Java #AI #Innovation #SoftwareDevelopment #Curiosity

🚀 AI is helping me write code faster… but here’s what actually made me a better developer 💭 Earlier, I used to focus only on writing code. ✨ Now I’m learning how to write better, production-ready code. Here’s what I explored recently: 🧠 Caching (Redis vs Caffeine) → Learned when to use in-memory vs distributed caching based on system design 🧪 Unit Testing & Code Coverage → Writing test cases using JUnit → Measuring code quality with JaCoCo 🤖 AI in Development (Cursor) → Used AI to write cleaner and faster code → Helpful in refactoring and improving code quality 🔥 Biggest Learning: Writing code is easy. Writing scalable, testable, and maintainable code is the real skill. 💬 Do you use AI while coding, or do you prefer writing everything manually? #Java #SpringBoot #AI #BackendDevelopment #CleanCode #LearningInPublic

The Great Convergence: Java Meets AI Engineering Are you building a demo for the weekend… or a system that survives the next 10 years? Because right now, the industry is splitting. Python dominates the research world. But enterprises are asking a different question: “Are we rewriting our entire backend just to add AI?” The answer? Probably not. The shift is already happening We’ve moved from: CRUD systems → Intelligent systems → Autonomous systems What used to be a “User Service” is now expected to: • predict behavior • automate decisions • understand context If it doesn’t… it starts to look outdated. Why Java is back in the conversation The old argument was: “Java doesn’t have the AI ecosystem.” That’s changing fast — some would say it already has. According to a 2026 report from Azul, 62% of enterprises are already using Java to power AI functionality. That’s not experimentation. That’s production. Frameworks like: • Spring AI • LangChain4j • LangGraph4j …are making LLMs feel like native JVM components. Not scripts. Not experiments. Actual production systems. This is bigger than chatbots We’re now building systems that can: • Search by meaning, not keywords • Call real business logic • Adapt workflows when things break That’s not “AI as a feature.” That’s AI as infrastructure. The real distinction Python is great for exploring ideas. Java is built for running the ones that matter. If you’re a Java developer, you don’t need to pivot away. You need to lean in. Because the next generation of AI systems won’t live in notebooks. They’ll live inside the systems that already run the world. So the real question is: Are you building something cool… or something that lasts? #Java #LangChain4j #LangGraph4j #SpringAI #AI #SoftwareEngineering #GenerativeAI #SpringAI #EnterpriseTech

Found a new term: Faith Coding. It’s when you trust your AI tools enough (have faith in them 😂) to develop a working codebase from requirements, without constantly checking every line of code along the way (E2E, 100% committed by AI). Last weekend, I led 10+ mid- to senior-level SWE on a backend case study using Java, Spring Boot, and Oracle. We started by turning the requirement spec into a SPEC.md and API contract using Opus 4.7. From there, we used those two documents as the source of truth and let GPT 5.5 develop the backend end to end until the API endpoints were usable and secured with JWT authentication. Honestly, it was impressive to see how the models generated the codebase and got it through the first iteration with only a few risks. Not perfect, but what first iteration ever is? For QA, we also used GPT 5.5 to test edge cases. Most of the remaining risks were around Flyway and migration behavior, which we intentionally excluded from the actual branch to prevent schema changes during boot-up. As an engineer, it still feels unnatural to build something without constantly looking at the code. But with the recent progress in LLMs, maybe some features or tasks can be “faith coded” the same way we write programs without looking at the assembly or binary output. With AI, we may start writing programs through natural language without always inspecting the intermediary code, whatever the target language is. It’s a fun framework to explore, but it still needs better grounding, stronger validation, and tighter engineering controls. What do you think? #AI #Native #Enginering #AINE #LLM #Cursor

I thought AI was giving me irrelevant answers. 🤔 Realized… I wasn’t asking the right questions 😅 Just explored a too good technique in prompt engineering. ________________________ RATCOFF: 🔥 R → Role A → Audience T → Task C → Context O → Output format F → Few-shot examples F → Further constraints _______________________ ✅ RATCOFF Prompt: Role: You are a senior backend engineer Audience: A beginner Java developer Task: Explain REST APIs Context: They know basic Java but no web concepts Output format: Simple explanation + real-world analogy + small code example Few-shot examples: Explain like you're teaching a junior in a team(add your desirable example) Further constraints: Keep it under 200 words, avoid complex jargo __________________________________________________ and from then..the tools started being gun shot. i surprisingly found that i basically follow the same pattern when i train my very junior developers. setting context...Define constraints...etc.. then I realized that we have to Stop asking AI questions. Start designing instructions..!! #promptengineering #AI #java #systemdesign #llm #prompt #developer

I’ve been exploring Spring AI recently, and it’s making AI integration in Java feel much more practical. Previously, working with AI in backend applications often involved handling raw API calls, prompt management, retries, and a significant amount of additional code. With Spring AI, the process feels much more structured and familiar. You can integrate large language models (LLMs), build chat-based features, or implement retrieval-augmented generation (RAG) using your own data—all within a Spring Boot application. What stands out to me includes: - Clean abstractions that align well with Spring style - Easy integration with various AI providers - The ability to treat AI as just another service in your application I am still learning and experimenting, but it feels like a promising direction for Java developers looking to build AI-powered features without needing to switch ecosystems. #SpringAI #java #backend #AI

Most people learn a language. Few learn how to think with it. Scala is not just about syntax — it’s about a mindset shift. From writing instructions → to describing outcomes. From mutable state → to predictable systems. From quick fixes → to scalable thinking. At first, it feels hard. Because it forces you to slow down… and think clearly. But once it clicks — you stop chasing bugs, and start designing better systems. You don’t just write code anymore. You build logic that holds under pressure. And that’s what separates a developer from an engineer. #Scala #FunctionalProgramming #SoftwareEngineering #CleanCode #BackendDevelopment

Recently I’ve been exploring how to integrate LLM-based AI systems into Java backend applications. One interesting challenge was designing a clean architecture where the backend communicates with AI services without making the system tightly coupled. What I learned: • Keep AI logic completely separate from core business logic • Use REST APIs as a bridge between backend and AI services • Design for scalability from the beginning (AI calls can be expensive and slow) It’s exciting to see how traditional backend engineering is evolving with AI. Would you say AI is becoming a core part of backend systems now?

For my second blog post, I found myself asking a new question: As AI fundamentally reshapes how we build software, what lies beyond Domain-Driven Design (DDD)? Looking at the enterprise practices of Netflix and Palantir, I came to the conclusion that the answer is "Ontology." To validate this theory, I built an Ontology-Driven Development (ODD) PoC. In this new article, I walk through the mechanics of this new development methodology, exploring: ◾ The core concept of defining a domain's essence using W3C standards (RDF/SHACL) so AI can perfectly understand the business. ◾ The design reasoning behind implementing ontology-driven domain models as immutable Java records. ◾ A practical demonstration of "Operational Intelligence," showing how semantic knowledge prevents LLM hallucinations in a complex Text-to-SQL use case. I would highly appreciate your perspectives on this approach.

Getting started with prompts is straightforward. Designing production-ready GenAI systems is a completely different challenge. When I was building GenAI systems with Java and Spring AI, I realized the real complexity comes from: 👉 how you design the system around the LLM For example, a production-ready RAG pipeline is not just: - retrieve documents - send to LLM It often involves: - query transformation / expansion - metadata filtering - re-ranking - pre/post-processing - dynamic updates to the knowledge base The same applies to AI agents: It’s not just tool calling — you also need to think about: - workflow design (autonomous vs chained) - context handling (chat memory, long-term memory) - failure handling and observability This is exactly what I focused on in my latest course: 👉 Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents If you're working with Java / Spring Boot and exploring GenAI, you might find it useful. https://lnkd.in/da8YikXF #genai #springai #java #aiagents #rag