Video assistant

Searching a long video by watching it is slow.

An assistant answers natural-language questions about one video. It uses OpenAI, and it works from the information in the video's transcript, so answers are grounded in what the video says, not the model's general knowledge. Each answer includes timestamps, so the user can jump straight to the moment it came from.

None.

Developed and deployed an AI assistant that allows users to ask natural-language questions about individual video content and receive contextually relevant answers grounded in information extracted from the video.

Implemented the assistant using a Retrieval-Augmented Generation (RAG) approach so that responses were generated using relevant video context rather than relying solely on the language model's general knowledge.

Included timestamp references in generated responses, enabling users to navigate directly to relevant portions of a video associated with the answer.

This transformed the interaction model from manually searching through video content to querying the video's information conversationally and navigating directly to relevant moments.

Searching a video becomes asking it a question.