When AI Writes Buggy Code — How to Read It Critically

The AI will hand you code that looks right but isn't. Here are the most common bugs and the habits that catch them before they bite.

35 min · Reviewed 2026

Confident and Wrong

Here is the single most important thing to know: AI-generated code can be completely convincing and completely broken. The code compiles, reads smoothly, and does not match reality. Your job is to assume this happens and check for it.

The big five AI coding bugs

Fabricated APIs: calling a function or library method that does not exist
Wrong library version: using syntax from an older or newer release
Off-by-one errors: loops that skip the first or last item
Missing edge cases: empty inputs, null values, zero-length arrays
Silent type mismatches: string where a number was expected

Example of a fabricated API

# AI often writes this when you ask to get JSON from a URL: import requests data = requests.get_json("https://api.example.com/data") # Problem: requests.get_json() does not exist. # The real API is: data = requests.get("https://api.example.com/data").json()A totally plausible-looking function call that will fail at runtime. This is the #1 AI coding bug.

The four-question review

Does every function call exist in the real library?
Is there a test or example that proves this works?
What does the code do if every input is empty?
What does the code do if every input is wrong?

Running code is the ultimate test

Reading catches some bugs. Running catches the rest. The moment you have code you think works, execute it with real inputs, then try weird ones. Nothing builds your instincts faster than watching AI code crash on inputs it clearly did not think about.

What to do when you catch a bug

Tell the AI exactly what went wrong — paste the error
Say what you expected to happen
Ask it to fix only that issue, not rewrite everything
Re-run to confirm the fix

AI will lie to you about code. Not because it wants to, but because it has seen too many confident answers.
— A senior developer

The big idea: treat AI code like code from a very fast, very confident intern. Read it, test it, challenge it. Never ship what you have not run.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coding-reading-buggy-ai-code-builders

What is the main idea of "When AI Writes Buggy Code — How to Read It Critically"?
1. The AI will hand you code that looks right but isn't. Here are the most common bugs and the habits that catch them before they bite.
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 "When AI Writes Buggy Code — How to Read It Critically"?
1. off-by-one
2. hallucination
3. fake APIs
4. code review
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. Fabricated APIs: calling a function or library method that does not exist
4. Use the first answer without checking it
What should a careful learner remember about "Hallucinated imports are everywhere"?
1. Use AI to draft or organize ideas about hallucination, then verify before acting.
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 "When AI Writes Buggy Code — How to Read It Critically" 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. Wrong library version: using syntax from an older or newer release

← Back to interactive lesson

Tendril · Builders · AI-Assisted Coding

When AI Writes Buggy Code — How to Read It Critically

The AI will hand you code that looks right but isn't. Here are the most common bugs and the habits that catch them before they bite.

35 min · Reviewed 2026

Confident and Wrong

The big five AI coding bugs

Fabricated APIs: calling a function or library method that does not exist
Wrong library version: using syntax from an older or newer release
Off-by-one errors: loops that skip the first or last item
Missing edge cases: empty inputs, null values, zero-length arrays
Silent type mismatches: string where a number was expected

Example of a fabricated API

# AI often writes this when you ask to get JSON from a URL: import requests data = requests.get_json("https://api.example.com/data") # Problem: requests.get_json() does not exist. # The real API is: data = requests.get("https://api.example.com/data").json()A totally plausible-looking function call that will fail at runtime. This is the #1 AI coding bug.

The four-question review

Does every function call exist in the real library?
Is there a test or example that proves this works?
What does the code do if every input is empty?
What does the code do if every input is wrong?

Running code is the ultimate test

What to do when you catch a bug

Tell the AI exactly what went wrong — paste the error
Say what you expected to happen
Ask it to fix only that issue, not rewrite everything
Re-run to confirm the fix

AI will lie to you about code. Not because it wants to, but because it has seen too many confident answers.
— A senior developer

The big idea: treat AI code like code from a very fast, very confident intern. Read it, test it, challenge it. Never ship what you have not run.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coding-reading-buggy-ai-code-builders

What is the main idea of "When AI Writes Buggy Code — How to Read It Critically"?
1. The AI will hand you code that looks right but isn't. Here are the most common bugs and the habits that catch them before they bite.
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 "When AI Writes Buggy Code — How to Read It Critically"?
1. off-by-one
2. hallucination
3. fake APIs
4. code review
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. Fabricated APIs: calling a function or library method that does not exist
4. Use the first answer without checking it
What should a careful learner remember about "Hallucinated imports are everywhere"?
1. Use AI to draft or organize ideas about hallucination, then verify before acting.
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 "When AI Writes Buggy Code — How to Read It Critically" 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. Wrong library version: using syntax from an older or newer release

← Back to interactive lesson