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Multimodal and document AI flashcards

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Multimodal GenAI and document AI

  1. Front
    Why is parsing quality the usual cap on document RAG?
    Back
    Tables, reading order, scans and charts break text extraction; if the fact never reaches the chunk, nothing downstream can recover it.
  2. Front
    How does a vision-language model take in an image, and why does that cost money?
    Back
    A vision encoder cuts the image into patches, a projection maps them into the LLM's token space, and each patch becomes input tokens. More or larger images means more tokens, latency and spend.
  3. Front
    Name the four ways to read a document page.
    Back
    Text-layer extraction, OCR plus layout analysis, VLM page reading, and page-image retrieval. Start with the cheapest that passes your evaluation and route hard pages up.
  4. Front
    Users report wrong numbers from a table. How do you locate the failure?
    Back
    Open the chunk the retriever returned. Fact missing or scrambled: parsing. Right chunk not retrieved: retrieval. Right chunk in the prompt but wrong answer: generation. Fix the earliest broken layer.
  5. Front
    How should a table be chunked?
    Back
    Keep it whole, or split by row groups and repeat the header and unit in every chunk. Store it as Markdown, HTML or records with column names, plus page and section metadata.
  6. Front
    Why flag chart captions written by a VLM?
    Back
    They are model-generated, not the document's words, and can misread exact values. Labelling them keeps readers and later checks from treating them as source text.
  7. Front
    What is a sound evaluation for a document extraction pipeline?
    Back
    Field-level accuracy on a labelled sample, page-level retrieval recall, programmatic number checks (line items sum to the total), and a human review path for low-confidence fields.
  8. Front
    What privacy risk comes with VLM page reading?
    Back
    A page image sends everything on the page to a hosted API. Check retention and training terms, redact where possible, or self-host for sensitive documents.