RAG retrieves the most relevant passages or clips from a knowledge store, then hands them to the model as context for its answer. It curbs hallucination, keeps responses current, and lets a model use private data it was never trained on.
The book · Volume 6 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Models and Inference to Computer Vision, Speech, Search, and Generative Video
RAG is how a model answers questions about your own video archive instead of guessing from memory, and most failures come from weak retrieval rather than the model. Volume 6 covers RAG in detail: search and retrieval over a video archive, ASR transcripts as indexable text, computer vision tags as metadata and the lifecycle of an AI feature. Written by Nikolay Sapunov, CEO at Fora Soft.
Building RAG search over your video archive and unsure how to index it?
Tell us about your archive and the questions users ask, and we will sketch the indexing pipeline, retrieval design and a cost estimate. 250+ video products shipped since 2005.
