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 ↗AI & LANGUAGE MODELS
Neuro Browser AI LLM
A practical evaluation track for language-model reading assistants: traceability, unsupported answers, schema checks, and reviewer agreement.
TOPIC GUIDE / 02 · READ, QUESTION, EXPLORE
Create a small evaluation set
Collect non-sensitive passages representing easy cases, ambiguous cases, and questions the text cannot answer. Write the expected evidence before running a model. Use the same material across configurations, and distinguish a response that looks polished from a response that follows the task. Keep model identifiers, prompt versions, and evaluation notes together.
Review disagreements, not just totals
A combined score can hide the difference between a formatting mistake and an invented claim. Group errors by their consequence, inspect examples, and identify whether the fix belongs in the prompt, source selection, interface, or review process. Do not use a language model’s self-reported confidence as proof that its answer is correct.
Three questions to take with you
01Which errors matter most in this workflow?
02Can each output be traced to input evidence?
03Did the evaluation include unanswerable questions?
Reference for this topic: NIST guidance on AI risks and trustworthiness. Read the original context alongside the guide.