Why ChatGPT Confidently Suggests Code That Doesn't Run

AI chatbots can't actually run your code — they pattern-match what code usually looks like, which sometimes invents APIs that don't exist.

7 min · Reviewed 2026

The big idea

When ChatGPT or Claude writes code, it's predicting tokens, not executing them. It's seen millions of similar snippets and stitches together what looks right. That means imported functions might not exist and library versions might be wrong — confidently.

Some examples

Claude suggests `array.sortBy()` in JavaScript — but that method doesn't exist in vanilla JS, only in Lodash.
ChatGPT writes `from openai import ChatCompletion` using an old API style that broke in v1.0.
Copilot autocompletes a `df.merge_asof_unique()` pandas call that isn't real.
Cursor invents a Tailwind class like `bg-gradient-rainbow` that doesn't ship with the framework.

Try it!

Ask an AI for a small script using a library you know well. Read carefully — try to catch one made-up function before you run the code.

Practice this safely

Try this with a school, hobby, or family example where the stakes are low. Use the AI output as a draft you can question, not as the final answer.

Ask AI to explain hallucination in plain language, then underline anything that sounds uncertain or too broad.
Give it one detail from "Why ChatGPT Confidently Suggests Code That Doesn't Run" and ask for two possible next steps plus one reason each step might be wrong.
Check API invention 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-builders-aicoding-ai-cant-run-your-code-r8a8-teen

What is the main idea of "Why ChatGPT Confidently Suggests Code That Doesn't Run"?
1. AI chatbots can't actually run your code — they pattern-match what code usually looks like, which sometimes invents APIs that don't exist.
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 "Why ChatGPT Confidently Suggests Code That Doesn't Run"?
1. API invention
2. hallucination
3. verify code
4. code execution
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. Claude suggests `array.sortBy()` in JavaScript — but that method doesn't exist in vanilla JS, only in Lodash.
4. Use the first answer without checking it
What should a careful learner remember about "The rule"?
1. Always run AI-generated code before trusting it. Hallucinated APIs look identical to real ones.
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 the AI answer as a draft, then check it against a reliable source.
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 "Why ChatGPT Confidently Suggests Code That Doesn't Run" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Use the first answer without checking it
4. ChatGPT writes `from openai import ChatCompletion` using an old API style that broke in v1.0.

← Back to interactive lesson

Tendril · Builders · AI-Assisted Coding

Why ChatGPT Confidently Suggests Code That Doesn't Run

AI chatbots can't actually run your code — they pattern-match what code usually looks like, which sometimes invents APIs that don't exist.

7 min · Reviewed 2026

The big idea

Some examples

Claude suggests `array.sortBy()` in JavaScript — but that method doesn't exist in vanilla JS, only in Lodash.
ChatGPT writes `from openai import ChatCompletion` using an old API style that broke in v1.0.
Copilot autocompletes a `df.merge_asof_unique()` pandas call that isn't real.
Cursor invents a Tailwind class like `bg-gradient-rainbow` that doesn't ship with the framework.

Try it!

Ask an AI for a small script using a library you know well. Read carefully — try to catch one made-up function before you run the code.

Practice this safely

Try this with a school, hobby, or family example where the stakes are low. Use the AI output as a draft you can question, not as the final answer.

Ask AI to explain hallucination in plain language, then underline anything that sounds uncertain or too broad.
Give it one detail from "Why ChatGPT Confidently Suggests Code That Doesn't Run" and ask for two possible next steps plus one reason each step might be wrong.
Check API invention 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-builders-aicoding-ai-cant-run-your-code-r8a8-teen

What is the main idea of "Why ChatGPT Confidently Suggests Code That Doesn't Run"?
1. AI chatbots can't actually run your code — they pattern-match what code usually looks like, which sometimes invents APIs that don't exist.
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 "Why ChatGPT Confidently Suggests Code That Doesn't Run"?
1. API invention
2. hallucination
3. verify code
4. code execution
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. Claude suggests `array.sortBy()` in JavaScript — but that method doesn't exist in vanilla JS, only in Lodash.
4. Use the first answer without checking it
What should a careful learner remember about "The rule"?
1. Always run AI-generated code before trusting it. Hallucinated APIs look identical to real ones.
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 the AI answer as a draft, then check it against a reliable source.
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 "Why ChatGPT Confidently Suggests Code That Doesn't Run" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Use the first answer without checking it
4. ChatGPT writes `from openai import ChatCompletion` using an old API style that broke in v1.0.

← Back to interactive lesson