🚀 Bridging Full-Stack Java + AI/ML + RAG (Retrieval Augmented Generation)

As a Java Full-Stack Developer, I’ve spent years building robust backend systems using Spring Boot, microservices, and reactive stacks. But lately, I’ve been diving headfirst into combining AI/ML + RAG architectures to build smarter apps.

Here’s what I’m building now:
• ⚙️ A proof-of-concept AI-powered knowledge assistant that uses RAG to fetch relevant snippets from large document corpora, then uses a Transformer model to synthesize answers.
• Backend is in Java (Spring Boot, WebFlux), with integrations into vector stores / embeddings (e.g. FAISS, Pinecone) and LLM APIs.
• On the frontend, I’m prototyping a React UI that supports conversational querying + context retention.

Why this matters:
• Many systems today just hand over raw LLM responses; by combining retrieval + reasoning, we reduce hallucinations and increase relevance.
• This fusion (Java full-stack + AI + RAG) is rare and powerful — it’s where modern enterprise applications are heading.

What I’m learning next:
• Fine-tuning domain-specific embeddings
• Better context-window management
• Efficient caching & real-time updates