confidence scores only matter if they help you decide what to automate.
for document extraction, that usually means knowing how much work you can safely accept at a given precision target. in our latest post, we look at confidence scoring through that lens, including:
✅️ confidence cutoffs
✅️ precision vs. recall
✅️ score coverage
✅️ score granularity
✅️ human review volume
using ExtractBench, we compare how different extraction systems perform after confidence filtering. at a 97% precision target, LlamaParse Agentic Plus reached 66.48% recall on expected fields after filtering.
the useful part of a confidence score isn’t the number itself. it’s whether you can use it to control automation and review in production.
👉️ read the full post: