Video RAG Definition

Why video RAG matters

Language models cannot read a video library. Even models that accept video input are limited to a handful of clips per request, and feeding a ten-thousand-asset archive into a prompt is neither technically possible nor economically sensible. Without retrieval, an assistant asked "what did our CEO say about pricing last quarter" can only guess.

Video RAG solves this the way text RAG solved document question answering: retrieve first, generate second. A small number of relevant segments are pulled from an index, and only those segments become context for the model. The answer is grounded in actual footage and can cite the exact timecodes it came from.

How video RAG works

Indexing. Video is segmented into shots or fixed windows. Each segment is enriched with multiple representations: a multimodal embedding of the visual content, a transcript of speech, extracted on-screen text, and structured tags. Embeddings go into a vector index; text goes into a keyword index.

Retrieval. A user question is converted into a query against both indexes. Hybrid retrieval — combining vector similarity with keyword matching — outperforms either alone, because embeddings capture meaning while keyword search reliably catches names, product codes, and rare terms that embeddings blur.

Reranking. The top candidates, usually fifty or so, are rescored by a more expensive model that judges actual relevance to the question. This step matters more in video than in text, because visual similarity produces plausible-looking but irrelevant matches.

Generation. The surviving segments — their transcripts, descriptions, and timecodes — are assembled into a prompt. The model answers using only that context and returns references to the source clips.

What makes video harder than text

Video segments have no natural paragraph boundaries, so chunking decisions are consequential: too short and a segment lacks context, too long and retrieval returns mostly irrelevant material. Meaning is also distributed across modalities — the answer to a question may live in the picture, the dialogue, a graphic, or the combination of all three, so a single-modality index systematically misses results.

Temporal reasoning adds another difficulty. Questions like "what happened right after the demo failed" require the system to retrieve a segment and then reason about adjacent ones, which demands that the index preserve ordering and neighbor links.

Evaluating a video RAG system

Measure retrieval and generation separately. Retrieval quality is recall at k: does the correct segment appear in the candidates at all? No amount of prompt engineering fixes a segment that was never retrieved. Generation quality is groundedness: does every claim in the answer trace to a retrieved segment, and are the cited timecodes correct? Requiring citations makes hallucination visible and verifiable.

How ShotAI relates to video RAG

ShotAI performs the indexing and retrieval layers that video RAG depends on — shot-level segmentation, multimodal embeddings, transcription, and hybrid search — returning time-coded segments that can ground an AI assistant's answers about a video library.