AutoRAG-Optimizer is a from-scratch ML systems project designed to systematically evaluate Retrieval-Augmented Generation (RAG) pipelines under controlled experimental conditions. The framework enables benchmarking of retrieval strategies, chunking configurations, prompt grounding approaches, and computational tradeoffs using real QA datasets.