Reviewing AI-Generated Work Before It Ships

AI-generated work arrives looking finished. The sentences are clean, the code compiles, the formatting is right. That polish is the risk. It invites a skim, and a skim is how a confident error reaches production.

Teams adopting AI for content and code are learning this in real time. The drafts get faster. The standard for shipping should not move. Review is where that standard gets applied.

Fluent is not the same as correct

A language model is built to produce plausible output. Plausible and true are different properties, and the gap between them is exactly what a reviewer closes.

Human drafts fail in familiar ways: typos, gaps, unclear thinking. AI drafts fail quietly. A made-up statistic reads like a real one. A function that does not exist is called with confidence. So the review has to be different, too. You are not proofreading. You are auditing.

Verify claims and catch invented specifics

Start with anything that can be checked. Numbers, dates, names, quotes, version numbers, product features, URLs, API methods. Each one needs a source you can open yourself, not one the model points to.

Invented specifics are the most common failure, and they hide well. A case study gains a client that never existed. A blog post cites a study with a believable title. A code comment references a config flag from a library version nobody released.

Two rules help:

• If you cannot trace a specific to a source, cut it or replace it with something you can prove.
• If a claim makes the piece stronger and you did not supply it, treat it with extra suspicion.

Also check promises. Timelines, prices, guarantees, and outcomes need a human who is actually willing to stand behind them. A draft will happily commit your company to things you never decided.

Check the voice

Voice drifts toward the generic. Left alone, AI copy reaches for the same openers, the same rhythm, the same enthusiasm. It sounds like everyone, which means it sounds like no one.

Read the piece aloud. Would someone on your team say this sentence? Look for hype words, announcement framing, and long paragraphs that should be three short ones. Check that it leads with an outcome, not a feature, and that every sentence earns its place.

Voice is part of trust. Readers may not name what feels off, but they feel it.

Read code for assumptions

Generated code usually works on the happy path. Review should go looking for what it quietly assumes. Here is a small example:

function averageOrderValue(orders) {
  const total = orders.reduce((sum, o) => sum + o.total, 0);
  return total / orders.length;
}

It reads fine. It also assumes the list is never empty (that returns NaN), that every total is a number, that all orders share one currency, and that refunds are handled elsewhere. None of that is stated. A reviewer’s job is to surface those assumptions and decide whether each is true in your system.

Then run the code. Do not rely on reading alone. Check that dependencies exist and are the versions you use, that error paths behave, and that tests cover the edge cases, not just the example the model chose. If the AI wrote the tests, review those with the same care. Tests that mirror the implementation’s mistakes prove nothing.

Decide who signs off

“The team reviewed it” is not an owner. Every piece of AI-assisted work needs one named person who approves it and is accountable for it.

Match the reviewer to the risk. A subject expert checks the facts. An editor checks the voice. An engineer who knows the system checks the code. For low-stakes internal work, one reviewer may be enough. For anything customer-facing, legal, financial, or security-related, require a second set of eyes.

Write down who owns what. When the rule is clear, nobody has to guess whether someone else already looked.

The checklist

Keep this short enough to use every time:

1. Sources: Every number, quote, date, and name traces to something you opened.
2. Invented specifics: No clients, studies, features, functions, or flags you cannot confirm.
3. Promises: Timelines, prices, and outcomes are ones a person has approved.
4. Voice: It sounds like your team. No filler, no hype, no announcement framing.
5. Assumptions: Inputs, edge cases, and dependencies are stated or handled.
6. Execution: The code was run and the tests mean something.
7. Owner: One named person signed off.

Review keeps quality high

It is tempting to see review as the slow part, the bottleneck standing between a fast draft and a shipped result. That framing gets it backward. A draft that ships with an invented claim or an unchecked assumption is not fast. It is debt, and someone pays it later with a correction, a bug, or lost trust.

Review is also where AI becomes useful instead of risky. The model supplies speed. The reviewer supplies judgment, context, and accountability. Together they produce work neither would produce alone.

You can make review faster without making it weaker. Keep the checklist short. Give reviewers the sources up front. Put the checks in the workflow, not in someone’s memory. Spend human attention on the places where only a human can decide.

At Artasce, we use AI as a force multiplier, not a replacement for craft. If your team is working out where review belongs in its own process, we are glad to talk it through.