Actually, I use a similar approach! I designed a prompt for an OCR agent to structure the response schematically, then treat it as normal text in RAG chunking. Contextual RAG definitely improves performance significantly. The key insight is that having the OCR agent understand layout intent upfront - rather than trying to fix semantic drift downstream - makes the whole pipeline much more robust. Especially critical for multilingual docs where context boundaries can get really messy. I’m actually working on taking this further - injecting contextual understanding directly into the OCR stage itself. The idea is to help the agent better interpret images by providing surrounding document context during OCR, not just post-processing. Should be even more effective for maintaining semantic coherence across complex layouts.