Multimodal RAG is underused: stop converting audio and video to text first
victorialslocum · x · 2026-09-17
The author argues most retrieval pipelines still flatten everything to text first — podcast transcripts, PDF OCR, video captions — losing tone, layout, and on-screen action in the process. Multimodal embeddings let you index text, images, audio, and video in a shared embedding space and retrieve across modalities, and it's easier to build than expected:
- Index PDF pages as images and pass retrieved pages directly to a multimodal LLM
- Chunk audio with overlap and retrieve without transcripts, preserving tone, silence, and emphasis
- Split video into 15-second segments to search for actions, not just spoken words
Multimodal search and RAG remain among the most underutilized tools in AI right now.
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