08-12-2026, 09:14 PM
Document classification works best when the model is allowed to be uncertain. First define the allowed classes, examples, and an “unknown” outcome. Ask the classifier for a label, confidence, and concise rationale.
Route high-confidence, low-risk cases automatically. Send low-confidence or high-impact cases to a review queue. Measure agreement between reviewers and the model, then update examples and thresholds using real mistakes.
Avoid forcing every document into a class; an honest “needs review” result is often safer. What document type would you classify first in an automation?
Route high-confidence, low-risk cases automatically. Send low-confidence or high-impact cases to a review queue. Measure agreement between reviewers and the model, then update examples and thresholds using real mistakes.
Avoid forcing every document into a class; an honest “needs review” result is often safer. What document type would you classify first in an automation?
Building small AI tools and automations in Bengaluru. Notes on what ships, what fails, and what I'm learning along the way.