Imagine effortlessly searching through massive video archives, pinpointing exact moments with simple text prompts. Researchers are making this a reality with Retrieval Augmented Generation (RAG) for video. Previously, finding specific clips in vast libraries was a daunting task, relying on manual searches or complex keyword queries. Now, large language models (LLMs) are being used to intelligently search video content indexed by spoken words, visual descriptions, and other metadata. This new approach allows users to ask conversational questions like, "Show me moments of astronauts training underwater," or "Find footage of mission control during the Apollo 11 landing." The system translates these requests into targeted search queries, retrieves relevant video snippets, and even suggests potential video edits based on the retrieved footage. This technology has the potential to revolutionize how we interact with video libraries. Content creators, journalists, and researchers can quickly locate specific moments within hours of footage, saving valuable time and resources. While the technology is still under development, early results are promising. Experiments on a large NASA video archive demonstrate the system's ability to quickly and accurately pinpoint relevant clips. The system works by first splitting videos into short segments, then indexing each segment with rich metadata. When a user asks a question, the LLM generates multiple search queries, retrieves the best matching segments, and presents them to the user with precise timestamps. This eliminates the need for users to sift through hours of footage manually. However, like many AI systems, this approach faces challenges. Ensuring the accuracy of generated metadata is crucial, as the system relies heavily on this information. Also, the potential for 'hallucinations,' where the LLM generates incorrect or irrelevant information, remains an area of ongoing research. Furthermore, establishing standard benchmarks for evaluating these systems is essential for future development. The next steps involve creating comprehensive datasets for testing and developing more sophisticated ranking algorithms to ensure the most relevant video segments are always retrieved. This innovative approach to video search promises to transform how we explore and utilize vast video archives, opening up new creative workflows and AI-assisted content creation possibilities.