Reviewing AI Code in Your First Week: A Junior's Drill

The role changed. Junior devs used to write code first. Now AI writes code first. You review it.

Your first week on a repo is not about commits. It's about context. AI-generated PRs will appear in your queue. You need a calm, repeatable drill.

Here it is. It takes 30 minutes. It works on any AI-generated diff.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

MonkeyCode is an open-source AI assistant. It offers free model access and a free server option. That matters for juniors. You can experiment without touching your wallet.

Use it to explain a diff, draft a test, or challenge an assumption. Then verify everything with the drill below.

Step 1: Map the change surface

Do not read the whole repo. Read the diff.

git diff main...HEAD --stat

List every touched file. Group them by risk: config, logic, tests, docs. Spend your attention on logic and config. Docs are nice. They cannot break production.

Step 2: Interrogate the AI

Open MonkeyCode. Paste the largest diff hunk. Ask three questions:

1. What was the original behavior before this change?
2. What problem does this change solve?
3. Which edge cases are explicitly not handled?

The third question is the gold one. Most generated code answers it with silence. That silence is your review target.

Step 3: Run a smell detector

AI code often smells the same. Hardcoded secrets. Bare excepts. Magic numbers. Empty stubs.

Save this script as review_smells.py:

import re
import sys
from pathlib import Path

SMELLS = [
    (r'(?i)(password|secret|token)\s*=\s*["\'][^"\']+["\']', 'hardcoded secret'),
    (r'except\s*:\s*(#.*)?$', 'bare except'),
    (r'\d+\.\d{3,}', 'magic float'),
    (r'def\s+\w+\(.*\):\s*pass', 'unimplemented function'),
    (r'except\s+Exception\s*:\s*(pass|continue|return None)', 'silent failure'),
]

for path in sys.argv[1:]:
    for line_no, line in enumerate(Path(path).read_text().splitlines(), 1):
        for pattern, label in SMELLS:
            if re.search(pattern, line):
                print(f"{path}:{line_no} [{label}]: {line.strip()}")

Run it against changed files:

python review_smells.py app/ service/

Every hit is a discussion point. Do not auto-fix. Understand why it appeared.

Step 4: Write one high-value test

AI can generate test suites. Most are tautologies. They test the implementation, not the contract.

Pick the riskiest changed function. Write one test that fails if the old behavior returns. That test is your proof the change does what it claims.

MonkeyCode can generate your first draft. Keep the test small. Assert on outputs, not internals.

Step 5: Decide: accept, edit, or rewrite

Use this decision table:

AI code characteristic	Your action
Small function, no side effects	Accept after reading it twice
Duplicated logic across three spots	Extract and simplify
Bare except or silent pass	Fix now, no exceptions
Hardcoded config value	Move to environment variables
Style clashes with project conventions	Match the surrounding code
Unimplemented stub with pass	Delete or file a blocking issue

Write one short comment per decision. Do not rewrite large sections without evidence. The table keeps you honest.

Limitations

This drill does not replace a senior's review. It builds your context. It also fails when the codebase is tiny and the diff is huge. For a 100-file refactor, you need a different strategy: split the review by domain, not by file.

Who should not use this? Anyone who treats AI output as truth. The drill is only useful if you challenge the output.

Final thought

Your first week is not about proving you can write code. It is about proving you can verify code. AI hands you more code faster. Your value is in the review.

Try MonkeyCode's free tier. Point it at a diff. Ask it to explain the riskiest change. Then run this drill on the answer. You might surprise yourself.