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Google Interview: Multimodal Document QA System (EXPLAINED)

Company BasedLLMsHard25 min read

Hard Google company question — document QA with OCR, layout, retrieval, multimodal models, and table/chart grounding.

TL;DR — Quick Answer

Pipeline: layout-aware parsing (OCR + table extraction) → structure-preserving chunks with page/bbox metadata → hybrid retrieval → multimodal or text LLM with citation to source spans → validation layer for numeric answers against extracted tables. Handle scans with quality checks; route charts to specialized table/chart parsers; eval on labeled doc QA with exact match on numbers; abstain when parse confidence low. Scale on batch indexing; online path optimizes p95 with caching frequent docs.

The Interview Question

Google-style interview: Design a multimodal document QA system over PDFs, scans, and spreadsheets with low hallucination rate.

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