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Built a Retrieval-Augmented Generation system for document-based question answering using Qdrant as the vector store.
Evaluated 5 chunking strategies and 3 embedding models across a custom 200-document dataset using MLflow tracking. Reduced hallucination rate by 31% and improved answer relevance by 18% on RAGAS evaluation. Deployed as a containerized FastAPI service with Redis-based caching for retrieval latency.