Continuing from my previous post on AI + Java… A common thought after that is: 👉 “This looks interesting… but how do I actually use it in my current project?” Let’s break it down simply 👇 🧠 Most Spring Boot services today follow this flow: Client → Controller → Service → DB / APIs → Response Now, to integrate AI… 👉 You don’t need a new system 👉 You extend your existing service 💡 Just one addition: ➡️ Add an LLM call inside your service layer 📊 Updated flow: Client → Controller → Service (AI Orchestrator) → Fetch Data → Build Context → Call LLM → Return smarter response 💡 Key takeaway: AI is added inside the service layer NOT as a separate system 📌 Example use cases: Search API → smarter results Support API → auto replies Recommendation API → personalization Most systems don’t need a redesign. Just a small extension. 👉 Curious: Which API in your current project could be enhanced with AI? #Java #AI #SpringBoot #SoftwareArchitecture #BackendDevelopment
Keyur Patel
5d
Yeah the service layer placement makes sense until you actually run it in prod. The consistency thing is what people underestimate though. This isn't a deterministic function - same input, different output. That's fine for a chatbot, but inside business logic? The other thing I'd watch is latency, what happens when the model takes 4 seconds on a request users expect in under 500ms?
Alternative you can use https://www.intelliswarm.ai which is a layer on top of Spring AI which offers access to build in tools and enterprise ready features
A simple and clear explanation that helps understand that adopting AI is not magic (but it does magic ), especially for those who have been in the industry for quite some time. As you, @Hari Krisnnan S, and @Anvesh K mentioned, in practical production use cases, it is always need to evaluate when, where, and how to apply AI. This depends on several factors, including technical and architectural considerations as well as more importanly business needs.
intresting
Faisal Fayaz
6d
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This is NOT the api call flow. The controller calls service which calls DB which returns the data to service which returns data to controller for the response. Controller is where request/response meet; you're arrows are completely wrong
Interesting. But in real projects, I feel it’s not always that straightforward. Token limits, latency, and cost can become big factors, especially at scale. We probably can’t just call an LLM for every request. Feels like we need some filtering/caching layer around it rather than a direct plug into the service.