Jerry Liu(@jerryjliu0):This is a fantastic blog post from our research team on the importance of calibrated confidence scores. Here it's in the context of document extraction, it's also extremely important for more general agentic decision making (eg with Jev) When you set a confidence threshold, you can choose to automatically accept values above that threshold and do HITL review of values below the threshold. The higher the confidence threshold, the higher precision you're able to guarantee (e.g. confidence of 0.7 could mean 95% precision, confidence of 0.9 could mean 98% precision), but of course the more human review you'd have to do on false negatives. We've put in a lot of work to make sure our confidence scores are well calibrated and represents real uncertainty over complex documents in production. Come check out our blog: https://t.co/Mel0ScoCBv LlamaParse:

2026.09.24 19:42

This is a fantastic blog post from our research team on the importance of calibrated confidence scores. Here it's in the context of document extraction, it's also extremely important for more general agentic decision making (eg with Jev) When you set a confidence threshold, you can choose to automatically accept values above that threshold and do HITL review of values below the threshold. The higher the confidence threshold, the higher precision you're able to guarantee (e.g. confidence of 0.7 could mean 95% precision, confidence of 0.9 could mean 98% precision), but of course the more human review you'd have to do on false negatives. We've put in a lot of work to make sure our confidence scores are well calibrated and represents real uncertainty over complex documents in production. Come check out our blog: LlamaParse:

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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:

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