Module 4 · Career, projects and honesty Lesson 14 of 15
Limits, hallucinations and how to check
These tools predict plausible text. It is excellent at connecting two things and poor at logic: no real loops, no branching beyond a few rules, and volume limits that bite without warning 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 excellent at connecting two things and poor at logic: no real loops, no branching beyond a few rules, and volume limits that bite without warning
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.
Read the run history weekly for the first month. The failures are silent, and the history is the only place they show up.
Worked example
The moment you need a loop nested inside a condition, you have outgrown the canvas — and rebuilding it in code is the cheaper answer, not the harder one.
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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