Video RAG
Video RAG (retrieval-augmented generation) is a technique in which an AI assistant answers questions by first retrieving relevant video segments, transcripts or frames from a library and then using them as context for a language model's answer. Grounding answers in real content reduces made-up responses. Good systems also cite the source video and timestamp so users can check the answer.
Why it matters
Assistants that answer from general knowledge can make things up. Video RAG grounds answers in a company's own recordings, so customers and staff get answers they can verify. It turns webinars, demos and courses into a self-serve knowledge base without rewriting them as articles.
How it works
- Videos are transcribed and split into segments, and each segment is embedded and stored.
- When a user asks a question, it is embedded and matched to the most relevant segments.
- The matching transcript text, and sometimes frames, go to a language model as context.
- The model writes an answer and cites the source video and timestamp.
What is the difference between RAG and fine-tuning?
RAG gives a model relevant documents at the moment a question is asked, without changing the model. Fine-tuning retrains the model on new examples so the knowledge becomes part of it. RAG is easier to keep current, since a new video becomes searchable once it is indexed, and it can cite sources. Fine-tuning suits style or format changes more than fresh facts.
How do you build a chatbot that answers questions from videos?
Transcribe each video, split the transcripts into short timed segments, create embeddings and store them in a vector database. When a question arrives, retrieve the closest segments and pass them to a language model with an instruction to answer only from that context and cite timestamps. Test with real user questions and check that citations point to the right moment. This step needs no export when the library is already in FastPix: transcripts come back from the API at $0.048 per minute, ready for your team to chunk and embed.
Where it's used
SaaS products and corporate training teams use video RAG to build assistants that answer questions from recorded webinars, demos and courses, with links to the exact moment the answer appears.
By the numbers
- The paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, by Patrick Lewis and colleagues, which described the RAG approach, was first submitted to arXiv on May 22, 2020. (Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (arXiv))
