LLM data classification: a prompt is only the beginning
Build a label handbook, preserve supporting evidence, and route ambiguous outputs to a reviewable decision process.
Read the guide ↗DATA & DECISIONS
Neuro Browser AI LLM Data Classification
Build an understandable label system for text and documents, with evidence excerpts, unresolved cases, and a human review queue.
TOPIC GUIDE / 03 · READ, QUESTION, EXPLORE
Write a label handbook
Describe each label in terms a reviewer can apply to a source. Include what belongs, what does not, and a difficult boundary example. Decide whether one item can receive several labels. Treat missing evidence differently from an item that genuinely belongs outside the target collection. Keep the handbook version with every evaluated result.
Separate structure from judgment
Validate that the model returned an allowed label and the expected fields, then independently inspect the supporting text. Track disagreements between reviewers as well as errors against a reference answer. A clean JSON object cannot establish that the label is useful or fair. This topic page is a method guide, not a hosted classification tool.
Three questions to take with you
01Can two reviewers apply the same definition?
02Is the evidence actually present in the input?
03Where do ambiguous items go?
Reference for this topic: NIST guidance on AI risks and trustworthiness. Read the original context alongside the guide.