AI can generate hundreds of lines in seconds. But would you trust that code in production without reading it first?
Generated code can look clean and convincing and still carry bugs, security holes, or wrong business logic. It can pass a basic test and still fail in the real world. Generated is not the same as verified — and a passing test is evidence, not proof.
Review it the way you would review a colleague's pull request. Five checkpoints:
1. Correctness does it do the right thing?
2. Security who is allowed to call it?
3. Edge cases what happens at the limits?
4. Performance what does it cost at scale?
5. Maintainability will the next person follow it?
A review process that works:
UNDERSTAND -> INSPECT -> TEST -> REVIEW -> APPROVE
Understand what changed. Inspect the affected files against your existing conventions, dependencies and business rules. Run the tests and probe the edge cases. Read the security-sensitive parts closely. Then read the final diff — approving is the last step, not the first.
The Rails example in the video:
AI writes this.
class Payment < ApplicationRecord
def complete!
update!(status: "completed")
end
end
At a glance it looks fine. Then you ask the questions a reviewer asks. Can it complete the same payment twice? Is the caller authorized? Is this status transition even valid? What happens if two requests arrive at once?
class Payment < ApplicationRecord
def complete!
with_lock do
return false unless pending?
update!(status: "completed")
end
end
end
with_lock runs the block inside a transaction and locks the row, so concurrent attempts on the same record get serialized. Then you test both paths — the pending payment that completes, and the already-completed one that gets rejected.
Worth being precise about:
- A row lock does not solve every payment problem. External provider calls, idempotency, retries and distributed transactions all need separate thought.
- `return false unless pending?` is deliberately simplified. Production code should define explicit behaviour for an invalid transition — usually raising a domain error rather than returning false.
- The improved version assumes `pending?` exists and that your status model allows this transition.
Reviewing everything takes time, so focus first on authentication, authorization, database changes, external calls, and core business logic.
What you'll learn:
- Why AI-generated code needs human review
- How to inspect generated code systematically
- How to spot security and business-logic issues
- How to review edge cases
- How to validate with automated tests
- How to improve a generated implementation
- What to check before merging
Chapters:
00:00 — How to Review AI-Generated Code
00:10 — Why Generated Code Can Be Dangerous
00:25 — What Does Code Review Mean?
00:40 — A Practical Review Process
01:00 — Ruby on Rails Payment Example
01:25 — Benefits and Risks
01:45 — The Takeaway
AI can write the code. You own the quality.
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— #Astmation —
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