Lesson deck
Multimodal GenAI and document AI flashcards
Reviewing needs JavaScript. You can read every card in the list below.
Loading your cards...
Back to the lesson8 cards in this deck
Read all 8 cards as a list
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.