Sometimes AI Makes Up Code That Doesn't Actually Work

AI can invent function names that look real but aren't — always test the code.

5 min · Reviewed 2026

The big idea

AI is so confident that it sometimes invents code that LOOKS right but won't run. It might call a function that doesn't exist or use a tool the wrong way.

Some examples

AI says 'use the magicSort() function' — but no such function exists.
Code looks perfect but throws an error the second you run it.
AI might mix up two different programming languages.
Always run the code to see if it actually works.

Try it!

Ask AI for a tiny program. Try to run it. If it errors, ask AI to fix it. Try again.

Practice this safely

Try this with a low-stakes example and a trusted adult nearby. The goal is to notice how AI talks about hallucination, not to let it make the decision for you.

Ask AI to explain hallucination in plain language, then underline anything that sounds uncertain or too broad.
Give it one detail from "Sometimes AI Makes Up Code That Doesn't Actually Work" and ask for two possible next steps plus one reason each step might be wrong.
Check testing against a trusted source, teacher, adult, expert, or original document before you use it.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-explorers-ai-coding-AI-and-spotting-fake-code

What is the main idea of "Sometimes AI Makes Up Code That Doesn't Actually Work"?
1. AI can invent function names that look real but aren't — always test the code.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Sometimes AI Makes Up Code That Doesn't Actually Work"?
1. testing
2. hallucination
3. verification
4. unrelated shortcut
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. AI says 'use the magicSort() function' — but no such function exists.
4. Trust the first answer because it sounds confident
What should a careful learner remember about "The rule"?
1. Looks real isn't the same as IS real — always test!
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use short, concrete wording and ask a trusted adult when the stakes matter.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about hallucination be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about hallucination.
Which action would help you apply "Sometimes AI Makes Up Code That Doesn't Actually Work" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Trust the first answer because it sounds confident
4. Code looks perfect but throws an error the second you run it.

← Back to interactive lesson

Tendril · Explorers · AI-Assisted Coding

Sometimes AI Makes Up Code That Doesn't Actually Work

AI can invent function names that look real but aren't — always test the code.

5 min · Reviewed 2026

The big idea

AI is so confident that it sometimes invents code that LOOKS right but won't run. It might call a function that doesn't exist or use a tool the wrong way.

Some examples

AI says 'use the magicSort() function' — but no such function exists.
Code looks perfect but throws an error the second you run it.
AI might mix up two different programming languages.
Always run the code to see if it actually works.

Try it!

Ask AI for a tiny program. Try to run it. If it errors, ask AI to fix it. Try again.

Practice this safely

Try this with a low-stakes example and a trusted adult nearby. The goal is to notice how AI talks about hallucination, not to let it make the decision for you.

Ask AI to explain hallucination in plain language, then underline anything that sounds uncertain or too broad.
Give it one detail from "Sometimes AI Makes Up Code That Doesn't Actually Work" and ask for two possible next steps plus one reason each step might be wrong.
Check testing against a trusted source, teacher, adult, expert, or original document before you use it.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-explorers-ai-coding-AI-and-spotting-fake-code

What is the main idea of "Sometimes AI Makes Up Code That Doesn't Actually Work"?
1. AI can invent function names that look real but aren't — always test the code.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Sometimes AI Makes Up Code That Doesn't Actually Work"?
1. testing
2. hallucination
3. verification
4. unrelated shortcut
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. AI says 'use the magicSort() function' — but no such function exists.
4. Trust the first answer because it sounds confident
What should a careful learner remember about "The rule"?
1. Looks real isn't the same as IS real — always test!
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use short, concrete wording and ask a trusted adult when the stakes matter.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about hallucination be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about hallucination.
Which action would help you apply "Sometimes AI Makes Up Code That Doesn't Actually Work" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Trust the first answer because it sounds confident
4. Code looks perfect but throws an error the second you run it.

← Back to interactive lesson