Module 4 · Career, projects and honesty Lesson 14 of 15
Limits, hallucinations and how to check
These tools predict plausible text. It is careful, which is not the same as correct — a wrong number inside a careful paragraph is harder to spot, not easier That is a mechanism, not a moral failing — and the habit it demands is the habit of asking "where did this come from?" out loud, every time, before you use an answer.
The concept
A model does not look things up unless it has been given a way to look things up, and even then it can attach a real number to the wrong claim. It is careful, which is not the same as correct — a wrong number inside a careful paragraph is harder to spot, not easier
So the rule is: numbers, dates, names, citations and legal or medical claims get checked in a primary source before they leave your hands. Everything else — drafts, structure, explanations, practice — is fair game.
Three questions to ask before you trust an answer. Where did this come from? What would make it false? Who is the original source, and can I open it? If the third one has no answer, you have writing material, not facts.
On a long document, the fastest check is to ask for the page number with every claim and open two of them at random.
Worked example
Ask it to total a column in an attached spreadsheet and check the total yourself. Any model that is reading text can misread a column.
Your practice task
Ask for one statistic with its source, then open the source. Sometimes it exists. Sometimes the citation is invented, and the number is close enough to a real one to be dangerous. Either way you will remember the exercise.
Do this before moving on. Reading a solution teaches the shape of it; typing it teaches you.
Key takeaway
A model does not look things up unless it has been given a way to look things up, and even then it can attach a real number to the wrong claim.
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