This course covers evaluation, optimization, and deployment for Video RAG systems. Measuring retrieval quality and serving the pipeline reliably are what separate a demonstration from a system other people can use.

You start by improving retrieval quality with re-ranking, query reformulation, and chunking strategies, then address the problems that appear at scale, including index size, latency, and cost. You build an evaluation dataset and measure the system with precision, recall, and groundedness metrics, using the results to guide optimization rather than guesswork. The course closes by wrapping the pipeline in a FastAPI backend and a web interface for upload, search, and question answering. By the end of this course, you will be able to: 1. Apply re-ranking and query reformulation to improve retrieval relevance. 2. Scale retrieval across large video libraries while managing latency and cost. 3. Build an evaluation dataset for a Video RAG system. 4. Measure retrieval and answer quality using precision, recall, and groundedness. 5. Expose a Video RAG pipeline through a FastAPI backend. 6. Deliver a web interface for video upload, search, and question answering. Intended for learners who have completed the earlier courses in the Specialization. Enroll now to measure what your system retrieves, then deploy it.