Module 4 · Career, projects and honesty Lesson 14 of 15
Limits, hallucinations and how to check
These tools predict plausible text. A fast, efficient model gives you less depth on hard reasoning, and being concise makes a shallow answer look complete 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. A fast, efficient model gives you less depth on hard reasoning, and being concise makes a shallow answer look complete
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.
Common mistakes
For extraction, spot-check three rows against the original page. One wrong row in a table you are relying on is worse than no table at all.
Worked example
Ask a multi-step question and compare with a reasoning model. The fast answer will be shorter and may quietly skip the step you needed.
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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