Jerry Liu(@jerryjliu0):predicting model uncertainty is a hard problem. perfectly "calibrated" confidence scores are exact probability values on whether the output is correct. this is extremely important for agentic decision making, including document extraction. i made a sick video below showing how confidence scores can be used to choose decision thresholds and vary precision / recall. if you set a really high threshold, then you automate less, but more of the automated extraction is correct. if you set a low threshold, then you automate more, but there's more errors in the extraction. check out our blog!

2026.09.24 23:33

predicting model uncertainty is a hard problem. perfectly "calibrated" confidence scores are exact probability values on whether the output is correct. this is extremely important for agentic decision making, including document extraction. i made a sick video below showing how confidence scores can be used to choose decision thresholds and vary precision / recall. if you set a really high threshold, then you automate less, but more of the automated extraction is correct. if you set a low threshold, then you automate more, but there's more errors in the extraction. check out our blog!

LlamaIndex 🦙@llama_index

2026.09.24 18:41

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: