Verifying AI Output

AI-generated code looks confident even when it is wrong. The skill being tested is your ability to read, test, and trust-but-verify what the model produces.

Key ideas

  • —Models hallucinate plausible APIs and edge-case handling — assume nothing is correct until checked.
  • —Read the generated code line by line; trace a small example by hand.
  • —Test boundaries explicitly: empty inputs, single elements, duplicates, overflow.

Why confident output is dangerous

Language models generate fluent, well-formatted code that reads as authoritative regardless of correctness. They can invent library functions, mishandle empty inputs, or get an off-by-one wrong while sounding certain. Knowing that the surface polish is uncorrelated with correctness is the first step; the second is never shipping AI code you have not personally reasoned through.

A verification routine

Treat generated code as a draft from a fast but unreliable colleague. Read it line by line, trace a tiny input by hand, and probe the boundaries — empty array, single element, duplicates, maximum size — because that is exactly where models slip. Asking the model to justify each step or cite why it is correct also surfaces faulty reasoning before it reaches your submission.

Practice this

Sources

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