LlamaIndex Explains Using Confidence Scores to Control Document Extraction Automation

LlamaIndex argues that extraction confidence scores are useful only when they help decide what can be automated and what needs human review.

Using ExtractBench, the post compares extraction systems after confidence filtering, reporting that LlamaParse Agentic Plus reached 66.48% recall on expected fields at a 97% precision target.

The post covers confidence cutoffs, precision versus recall, score coverage, score granularity, and human review volume.