Multimodal RAG Interview Guide: Visual Documents, Embeddings, and Grounding

Multimodal RAG interviews address documents where critical information lives in tables, charts, and diagrams—not extractable text. Interviewers ask how you parse visual PDFs, when OCR suffices versus vision-language models, and how you chunk and index mixed modalities without losing structure.

Embedding strategies span CLIP-style joint spaces, separate text and image towers with fusion at query time, and page-level screenshots versus structured cell extraction. Candidates explain trade-offs in recall, storage cost, and interpretability of citations.

Grounding visual answers requires citing page regions, figure IDs, or structured cell coordinates—not vague references. Discuss UI patterns that show highlighted evidence and eval metrics for visual faithfulness beyond text-only RAG benchmarks.

Latency and cost explode when every query runs a large VLM over multiple page images. Strong answers include tiered pipelines: text retrieval first, selective visual reranking, and caching of parsed layouts for stable documents.

Failure modes include misread chart axes, OCR errors on scanned contracts, and hallucinated values from decorative images. Production engineers describe validation steps, confidence thresholds, and human review for high-stakes financial or medical visual documents.