The most valuable engineering skill in an AI-assisted workflow isn't writing code faster — it's catching the specific ways AI-generated code goes wrong.
1Fluent Text and Correct Logic Are Independent
A model's writing style stays confident and fluent whether the underlying logic is right or wrong — that fluency comes from how it generates text, not from any actual verification of correctness. Treat tone as zero evidence about correctness.
2Ask It to Find Problems, Not Just Solve Them
The same underlying model, asked to generate a solution versus asked to skeptically critique a solution, often produces different, more honest information — deliberately using the critique framing as a second pass is one of the highest-value review habits in an AI-assisted workflow.
3Step-by-Step Breakdown
Review It Like You Wrote It Yourself. AI-generated code fails differently than human code: it's confident-looking even when subtly wrong, it can silently invent a plausible-sounding API that doesn't exist, and it rarely says 'I'm not sure about this part.' Reviewing it needs a specific checklist, not just a vibe check.
Catch a Plausible-Looking but Wrong Claim. Ask the model to write a small function using a real third-party library, then specifically ask it to double check whether every method it used actually exists in that library's real API — this surfaces the gap between 'looks right' and 'is right'.
Why is AI-generated code that 'looks confident' not a reliable signal that it's correct?
- →The model's fluent, confident tone is a property of how it writes text, not evidence about whether the underlying logic or API usage is actually correct — the two are unrelated.
- →Confident-looking code is always generated faster, and faster generation is inherently less reliable.
Ask It to Argue Against Its Own Solution. A useful review technique: ask the model to critique its own output as if it were a skeptical senior reviewer, specifically looking for edge cases or incorrect assumptions.
The Review Habit Is the Actual Skill. Notice that neither exercise required you to write code by hand — the skill being practiced is the review habit: checking claims against real APIs, and deliberately asking for a skeptical second pass. Next lesson: taking a reviewed prototype the rest of the way to a real shipped feature.
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Accessibility (A11y)
1Explicitly Review AI-Generated Markup for Accessibility, Don't Assume It
AI-generated HTML/JSX often looks reasonable but frequently omits correct ARIA attributes, semantic elements, or focus management — treat accessibility as one of the specific things you check for, the same way you'd check for a nonexistent API method.
// Explicitly ask: "does this modal trap focus and set aria-modal correctly?"SEO Implications
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Target 'how to review AI generated code' and 'catching AI coding mistakes' as a distinct, practical skill topic
Readers want concrete review techniques they can apply immediately, not a general warning that 'AI can be wrong sometimes'.
Best Practices
Make Self-Verification an Explicit Second Prompt, Not an Assumption
Don't trust a first answer's API usage on faith — ask explicitly for a second pass confirming every method or option used is real and documented. This single habit catches a meaningful share of plausible-but-wrong AI output.
Frequent Bugs
Shipping AI-generated code that calls a method or option that doesn't actually exist in the library being used, because the code otherwise looked correct.
For any unfamiliar API usage in AI-generated code, explicitly ask the model (or check documentation directly) to confirm each method/option is real before merging — don't rely on the code merely looking plausible.
Real-World Examples
The Invented Option
An AI assistant generated code calling a `strict: true` option on a well-known parsing library — the option sounded plausible and matched the library's general style, but didn't actually exist, silently falling back to default behavior with no error. An explicit self-verification pass caught it before merge.
// Looked right: parser.parse(input, { strict: true })
// Reality: no such option exists; silently ignored