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Reviewing AI Output: The Critical Skill

5 min read

Appsec

AI-generated security advice can be confidently wrong. Verify every recommendation against OWASP and your threat model.

Dba

AI SQL can be syntactically correct and semantically disastrous. Always validate against your schema and indexes.

Reviewing AI Output: The Critical Skill

TL;DR

  • The single most valuable skill with AI is reviewing what it produces — not prompting it.
  • AI is confident and wrong. It will cite fake papers, write plausible-but-broken code, and miss edge cases.
  • Build a mental checklist. Treat every AI output as a draft that needs verification.

Think of AI as a very fast intern who never sleeps but sometimes hallucinates. The intern gets a lot done. You still have to check their work.

Why This Matters More Than Prompting

You can get decent output with mediocre prompts. You cannot get safe, correct output without good review. In 2025, the engineers who get burned are the ones who copy-paste without thinking. The ones who thrive are the ones who treat AI output as a starting point, not a deliverable.

What Goes Wrong (And How to Catch It)

1. Hallucinations

AI invents facts, APIs, and citations. It'll reference a function that doesn't exist or a library version that was never released.

Catch it: Verify external facts. Google the citation. Check the docs. Don't trust "I'm pretty sure this exists."

2. Subtle Logic Bugs

Code that compiles and looks right but has off-by-one errors, race conditions, or wrong assumptions.

Catch it: Trace through with real inputs. Write a test. Ask: "What happens when X is null, empty, or huge?"

3. Security Blind Spots

AI suggests eval() or string concatenation for SQL. It skips input validation. It proposes default credentials.

Catch it: Security-sensitivity checklist. Never trust AI for auth, crypto, or injection-prone code without review.

4. Outdated or Wrong Context

AI trained on older data might recommend deprecated patterns or APIs that changed.

Catch it: Cross-check with current docs. "Is this still the right approach in 2025?"

5. Missing Edge Cases

AI optimizes for the happy path. It forgets empty lists, time zones, and "what if the network fails?"

Catch it: Ask: "What could go wrong?" Run it with weird inputs.

A Simple Review Checklist

Before you ship anything AI-generated:

  • Did I verify facts, APIs, and citations?
  • Did I trace through the logic with real examples?
  • Are there security implications I need to check?
  • Does this match our current patterns and constraints?
  • What edge cases might break this?

Five questions. Two minutes. Saves you from a lot of embarrassment.

The Cost of Skipping Review

A junior dev once pasted AI-generated Terraform into production. The AI had invented a aws_s3_bucket_policy attribute that doesn't exist. The apply failed — no harm. Another dev pasted AI-generated SQL that dropped a table in a migration. They didn't read it. That one hurt.

Review isn't bureaucracy. It's the price of using a tool that's confident and wrong.

You paste AI-generated SQL into a migration. It runs. A week later you notice a table is missing. The AI had included a DROP statement you didn't read. Prod incident. Postmortem. Regret.

Click "Review first, then apply" to see the difference →

Quick Check

AI generates code that compiles and looks correct. What's the most important next step before shipping?

Do This Next

  1. Review one AI-generated artifact from this week (code, doc, email) using the checklist. Note what you would have missed.
  2. Create a personal "AI output review" template for your most common task (e.g., PR description, incident summary). Use it next time.