Extract meaning. Keep proof.
Design narrow text tasks, constrain untrusted content, and accept fields because the source supports them.
Journal collection
AI & LLM.
Automate interpretation. Keep the evidence.
Focused reading from the Extraction Journal. Start with the guide that matches the next decision in your workflow.
2 COMPLETE ARTICLESModel-assisted extraction needs a narrow task, reviewed examples, and a clear way to report uncertainty. These guides separate document recognition from interpretation and explain why well-formed output is not proof of a supported value. The AI article focuses on documents, layout, evaluation, and human review. The LLM article focuses on text, evidence references, untrusted instructions, and constrained handoffs. Read them together when a workflow moves from documents into a language-model step. Define missing-value behavior before tuning a prompt, and evaluate important fields individually. The aim is a candidate record that can be checked, not a confident response that fills every blank.
Documents into evidence.
Separate recognition from interpretation and build evaluation, validation, and human review into document extraction.