Video RAG Systems

This course covers the full Video RAG pipeline: ingestion, indexing, retrieval, and answer generation across a video collection. Moving from a single file to a searchable library is where a prototype becomes a usable system. You begin by building a repeatable ingestion pipeline and a structured knowledge base, then implement semantic, metadata, and hybrid retrieval strategies. You filter results by timestamp so queries return precise moments, and extend search across multiple videos. The course then connects retrieval to a large language model, with prompt design that keeps answers grounded, and builds a conversational assistant that resolves follow-up questions against stored history. By the end of this course, you will be able to: 1. Build a repeatable ingestion pipeline and structured video knowledge base. 2. Implement semantic, metadata, and hybrid retrieval against a vector database. 3. Filter and rank results by timestamp to return precise video segments. 4. Search across a multi-video collection with consistent relevance. 5. Connect retrieval to an LLM to generate grounded, cited answers. 6. Build a conversational video assistant that handles follow-up questions. Intended for learners who have completed Video RAG Foundations or have equivalent RAG and video processing experience. Enroll now to search a whole video library and get answers grounded in it.