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.
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AI Prompts Neuro Browser
Design prompts around a task, evidence, output structure, and an honest unknown state. Measure the result instead of collecting impressive wording.
TOPIC GUIDE / 02 · READ, QUESTION, EXPLORE
Use a reviewable prompt contract
A practical prompt names the material to read, the decision to make, and the evidence that must be returned. Add an explicit option for insufficient information. For example, ask a model to classify a document as a tutorial, research report, or unresolved item, then provide the passage behind its choice. Keep the label definitions outside the untrusted document.
Test boundary cases
Include a tutorial with no worked example, a paper abstract with no methods, and a source that fits two categories. Compare the output with a reviewer’s documented decision. Change one part of the prompt at a time and keep earlier results. A valid response shape is only a first check; the classification still needs to match the evidence.
Three questions to take with you
01Are allowed labels defined clearly?
02Can the model leave an answer unresolved?
03Is each label supported by the supplied text?
Reference for this topic: NIST guidance on AI risks and trustworthiness. Read the original context alongside the guide.