Few-Shot Learning for Document AI

Few-shot learning refers to the ability of a model to learn a new task from just a handful of examples, rather than requiring thousands of labelled training samples. In the context of document AI, this means showing the extraction system a few examples of what a correct extraction looks like — "on this page, the lease start date is here and reads '1 January 2025'" — and having the system generalize that pattern to unseen documents.

DocumentIQ implements few-shot learning through its annotation system. Users open a document in the PDF viewer, draw a bounding box over the relevant text, and map it to an extraction field. The system stores the selected text, its position, and the field association. During extraction, these annotations are injected into the LLM prompt as concrete examples: "In a similar document, 'Lease Start Date' was found at page 2 and read: '1 January 2025'." This gives the model grounded, real-world examples of the expected output format and location patterns without requiring any model fine-tuning.

The practical benefit is significant: instead of spending weeks building and maintaining extraction templates, a user can annotate three to five representative documents and achieve high extraction accuracy across an entire corpus of similar documents. The feedback loop further strengthens this — when a user corrects an extraction error, the corrected value can be fed back as an additional example during reprocessing, creating a lightweight learning cycle that improves over time.