Teach junior developers to read AI output the way they would read a stranger's pull request: assume it is plausible and unproven. Give them a repeatable routine, practice on real flawed examples, and make explaining the code aloud part of every review.
The shift in the job
When an assistant writes the first draft, the developer's main skill becomes judging code, not typing it. That is harder for juniors, who have seen fewer failures. A Stanford user study found that people using an AI assistant wrote significantly less secure code and felt more confident about it. It also found that people who trusted the assistant less and iterated on their prompts did better. Healthy doubt is a teachable habit.
A four part training plan
1. A fixed routine. Hand them the review checklist for AI-written pull requests: dependencies, authorization, input handling, secrets, tests, scope.
2. Trace one piece of data. Pick an input and follow it to every place it ends up. Doing this by hand builds the mental model that scanners cannot give.
3. Spot the flaw exercises. Collect short snippets with a planted problem: missing ownership check, string built SQL, hard-coded key, unchecked file path. Have juniors find them in ten minutes, then discuss.
4. Explain it back. In review, the author explains what the code does and why each dependency is there. If they cannot, the change is not ready.
Habits to build
- Never install a package without looking up its page.
- Ask the assistant why, then verify the answer against documentation.
- Write one test that should fail.
- Say "I do not know what this line does" out loud, and treat it as a finding, not a weakness.
What seniors owe them
Review juniors' AI-assisted changes with patience, and show your own process. Say what you checked and what you skipped. Keep the feedback specific, such as "this route has no owner check", rather than "be careful with AI".
Measure lightly
Track how many review comments catch real problems over time, and how many new dependencies get a written reason. Those numbers show whether the habit is forming without turning it into surveillance.
A sample session
Run a thirty minute session monthly. Show one real, anonymized AI-assisted pull request with a flaw that reached review. Let the group find it, then ask what routine step would have caught it sooner. Rotate who presents. Over a few months the team builds a shared library of failure patterns drawn from its own code.
Quick check
Should juniors avoid AI assistants until they are experienced?
That is a team decision. Many teams allow them with required review and an explain it back rule, since the habit of checking output matters more than the tool.
Sources
Last reviewed: 19 October 2026.
