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
LLM Neuro Browser
Learn the role of large language models in a research workflow and separate text generation from retrieval, verification, and action.
TOPIC GUIDE / 02 · READ, QUESTION, EXPLORE
Keep the pieces distinct
A language model, a source retrieval system, and a browser controller should not be treated as one invisible capability. Sketch where text comes from, what the model is asked to do, and who can act on its output. A response can be well formatted while still missing the intended evidence. Make the source boundary a visible part of the interface.
Start with a bounded reading task
Try asking for a list of claims from a supplied excerpt, each paired with a supporting passage and an uncertainty note. Include an excerpt that does not answer the question. Evaluate whether the workflow preserves that absence instead of filling it with plausible prose. Model selection comes after the task definition, not before it.
Three questions to take with you
01Is the model using supplied text or retrieved material?
02What happens when evidence is missing?
03Does the reviewer see the original passage?
Reference for this topic: NIST guidance on AI risks and trustworthiness. Read the original context alongside the guide.