What you will learn
OCR, Layout, Tables, Images, Human review.
Engineering the capability
Multimodal applications need provenance at a finer level than “this file.” For an invoice, retain page number, region or bounding box where available, extracted text, and the transformation that produced it. OCR can confuse characters, merge columns, or lose currency symbols. A language model can then confidently explain corrupted input.
Separate extraction from validation. Check arithmetic, required fields, allowed currencies, and consistency with the document. If a quantity or price is uncertain, ask for review instead of silently inventing a plausible value. Keep original evidence accessible to the reviewer.
Model-native document understanding and OCR-plus-text pipelines have different latency, privacy, and layout tradeoffs. Compare them on representative scans, photographs, and digital files. Define maximum page counts and file sizes; parsing unbounded uploads is an operational risk independent of model accuracy.
Worked case
An invoice has quantity 3, unit price 20, and total 60. Extraction returns quantity 8 while preserving the total. A simple arithmetic check detects the inconsistency even if all fields are valid numbers. The system should show the relevant region for correction, not overwrite the total to make its own extraction appear consistent.
Put it into practice
Continue with the next lab: validate invoice extraction. Build the artifact, record the failure cases, and explain the tradeoff before moving on.