MD RAGHIB

I’m an Information Technology engineering student deeply interested in the intersection of Software Engineering and Artificial Intelligence. I’m currently exploring LLMs, with a strong focus on turning them into practical, production-ready applications.

I don’t just run models—I build applications around them. My recent work has centered on Retrieval-Augmented Generation (RAG), where I design systems that improve factual grounding for LLM outputs.

I built a RAG system that chunks PDF data, stores it in vector stores (FAISS), and retrieves relevant context to ground LLM responses. For the generation layer, I used Google Gemini to produce answers grounded in the retrieved content.

I’m excited by the engineering challenges of prompt design, embeddings pipelines, and Azure GenAI workflows. I want to keep growing as a FastAPI and AI developer, shipping reliable LLM features that deliver accurate, user-ready results.