🚀 From Debugging Logs Manually → Building an AI that does it in seconds As a Java backend developer, I’ve always worked on APIs, databases, and scalable systems. But with AI booming everywhere, I got curious — can I build something meaningful in GenAI using Java? So I decided to experiment 👇 🔹 Built a mini project integrating: Java + GenAI + RAG 🔹 What I explored: Instead of just calling an LLM, I wanted to understand how real-world AI systems actually work — storing embeddings, retrieving context, and generating smarter responses. 🔹 Biggest learning: AI is not just about models — it’s about data pipelines, retrieval, and system design, which fits perfectly with backend engineering. 🎯 What it does: You send a log → and it returns: ✔ Root Cause ✔ Suggested Fix ✔ Severity ⚙️ How it works (architecture): 1️⃣ Log comes via API (/logs/analyze) 2️⃣ Converted into embeddings (vector representation) 3️⃣ Stored + searched in PostgreSQL (using cosine similarity) 4️⃣ Retrieves similar past logs (RAG) 5️⃣ Sends context + current log to LLM 6️⃣ Returns structured AI response 🧠 Tech Stack: • Java + Spring Boot • PostgreSQL (for embedding storage) • Custom cosine similarity search • LLM (via Groq API) • RAG-based architecture ⚡ Challenges I solved: • Handling embedding storage & JSON issues (real struggle 😅) • Designing similarity search without a dedicated vector DB • Structuring LLM output into strict JSON • Making backend + AI work together seamlessly 💭 Biggest takeaway: GenAI is not just about calling an API — it’s about combining backend systems + data + retrieval + AI 🎥 Sharing the architecture flow in the post 👇 Would love valuable feedback / thoughts! #Java #GenAI #LLM #RAG #BackendDevelopment #AIProjects #SpringBoot #PostgreSQL #SoftwareEngineering #Tech #Hiring
Keep up the good work!
Building similarity search without a dedicated vector DB is impressive. Also, structuring LLM output into strict JSON is underrated but critical for production use.