Reviewing AI Code Like a Senior Engineer

Reviewing AI-written PRs is a different sport from reviewing human ones. Learn the structured review workflow that catches AI-specific bugs, plus the questions that separate confident-looking trash from real engineering.

12 min · Reviewed 2026

Reading Is the Job Now

When AI writes most of your code, reviewing becomes the engineer's primary craft. AI-written PRs look polished — better commit messages, better names, better formatting than humans usually produce. They also fail in stereotyped ways humans rarely do. The review checklist must adapt.

Human PR vs. AI PR — what changes

Dimension	Human PR	AI PR
Surface polish	Variable	High — formatted, named well, comments present
Test coverage	Often skipped	Usually present (but may be tautological)
Off-by-ones	Common in custom logic	Common in loops AI generated
Hallucinated APIs	Rare	Common — calls to functions that don't exist in your codebase
Architectural fit	Usually consistent	May fight existing conventions
Risk of overconfidence	Self-aware	AI rarely flags its own uncertainty

The structured walk: 7 stops, in order

READ THE COMMIT MESSAGE — does it match the diff? AI-generated messages are often aspirational
READ THE DIFF SUMMARY — count files changed; if >10, push back for a smaller PR
VERIFY IMPORTS — every new import: is it real, is it the right version, is it on the allowlist?
TRACE A REQUEST — pick one user-visible flow and walk it end-to-end through the changes
CHECK EDGE CASES — does empty/null/zero/unicode have a test? Almost never on AI's first pass
RUN THE TESTS LOCALLY — green CI is necessary, not sufficient
READ THE TESTS — are they real tests, or assertions that mirror the implementation?

Five questions to interrogate AI code

# Pin these to your monitor:

1. "What was here before, and why?"
   — AI loves to delete code it doesn't understand the purpose of.

2. "What happens to this function on the most surprising input?"
   — Empty? Null? Unicode? Very large? Concurrent?

3. "Does this change touch any business logic that has documented reasons?"
   — Check ADRs, comments, related issues.

4. "Where in this PR would a malicious user attack?"
   — Even if the feature isn't security-critical, ask.

5. "Could I reproduce this PR's intent in 50% fewer lines?"
   — AI tends toward verbose; cut what doesn't earn its keep.Five questions you ask of every PR. Answers reveal whether the AI understood the task or merely shaped output to look like it did.

The single highest-value review prompt

# Run in a fresh chat with a different model than wrote the PR:

"You are a senior reviewer with no context on this PR's history.
Do not trust the description. Read only the diff.

For each changed file, list:
  1. What this change does (1 sentence)
  2. The single most likely bug introduced
  3. The single most likely regression risk in adjacent code

Do not be polite. Be blunt. List concrete line numbers."Cross-model adversarial review. Catches what same-family review misses.

What to push back on

Renames you didn't ask for — `getUser` -> `fetchUserById` everywhere is a code-churn red flag
New abstractions for one caller — premature generalization
Tests that mirror the implementation (`expect(result).toEqual(result)`)
Changes outside the stated scope of the PR
Comments that explain what the code does, not why

Approving vs. requesting changes

Signal	Action
Imports verified, tests cover edges, scope tight	Approve
Tests are tautological — match implementation 1:1	Request: "rewrite tests against the spec, not the code"
Diff includes unrelated changes	Request: "split into separate PRs"
Hallucinated function call	Request: "verify <function> exists and document its source"
Performance claim with no measurement	Request: "add benchmark or remove the claim"
Security-critical path with no human-written test	Block until written

An AI PR with no review is a commit by an intern who has never seen production.
— An engineering manager

The big idea: AI PRs look better than human ones and fail in different ways. The structured walk, the five questions, and cross-model adversarial review catch most of it. Reviewing well is now the engineer's most leveraged skill — what you catch in review is what doesn't ship to production.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coding-debug-code-review-workflows-creators

What is the core idea behind "Reviewing AI Code Like a Senior Engineer"?
1. Reviewing AI-written PRs is a different sport from reviewing human ones. Learn the structured review workflow that catches AI-specific bugs, plus the questions that separate confident-looking trash from real engineering.
2. read-only role
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
Which term best describes a foundational idea in "Reviewing AI Code Like a Senior Engineer"?
1. structured walk
2. code review
3. tautological test
4. scope creep
A learner studying Reviewing AI Code Like a Senior Engineer would need to understand which concept?
1. code review
2. tautological test
3. structured walk
4. scope creep
Which of these is directly relevant to Reviewing AI Code Like a Senior Engineer?
1. code review
2. structured walk
3. scope creep
4. tautological test
Which of the following is a key point about Reviewing AI Code Like a Senior Engineer?
1. READ THE COMMIT MESSAGE — does it match the diff? AI-generated messages are often aspirational
2. READ THE DIFF SUMMARY — count files changed; if >10, push back for a smaller PR
3. VERIFY IMPORTS — every new import: is it real, is it the right version, is it on the allowlist?
4. TRACE A REQUEST — pick one user-visible flow and walk it end-to-end through the changes
Which of these does NOT belong in a discussion of Reviewing AI Code Like a Senior Engineer?
1. READ THE DIFF SUMMARY — count files changed; if >10, push back for a smaller PR
2. VERIFY IMPORTS — every new import: is it real, is it the right version, is it on the allowlist?
3. READ THE COMMIT MESSAGE — does it match the diff? AI-generated messages are often aspirational
4. read-only role
Which statement is accurate regarding Reviewing AI Code Like a Senior Engineer?
1. New abstractions for one caller — premature generalization
2. Tests that mirror the implementation (`expect(result).toEqual(result)`)
3. Renames you didn't ask for — `getUser` -> `fetchUserById` everywhere is a code-churn red flag
4. Changes outside the stated scope of the PR
Which of these does NOT belong in a discussion of Reviewing AI Code Like a Senior Engineer?
1. Renames you didn't ask for — `getUser` -> `fetchUserById` everywhere is a code-churn red flag
2. read-only role
3. New abstractions for one caller — premature generalization
4. Tests that mirror the implementation (`expect(result).toEqual(result)`)
What is the key insight about "Use AI to review AI" in the context of Reviewing AI Code Like a Senior Engineer?
1. Tools like Cursor's bug-bot, GitHub Copilot's PR review, and Vercel Agent's code review give a useful second opinion.
2. read-only role
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
What is the key insight about "AI tends to refactor while it's there" in the context of Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. If you ask the agent to fix a bug, it may also rename a variable, reorder imports, and reformat a function.
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
Which statement accurately describes an aspect of Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
3. When AI writes most of your code, reviewing becomes the engineer's primary craft.
4. rubber duck debugging
What does working with Reviewing AI Code Like a Senior Engineer typically involve?
1. read-only role
2. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
3. rubber duck debugging
4. The big idea: AI PRs look better than human ones and fail in different ways.
Which best describes the scope of "Reviewing AI Code Like a Senior Engineer"?
1. It focuses on Reviewing AI-written PRs is a different sport from reviewing human ones. Learn the structured review
2. It is unrelated to ai-coding workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. Human PR vs. AI PR — what changes
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
Which section heading best belongs in a lesson about Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
3. The structured walk: 7 stops, in order
4. rubber duck debugging

← Back to interactive lesson

Tendril · Creators · AI-Assisted Coding

Reviewing AI Code Like a Senior Engineer

12 min · Reviewed 2026

Reading Is the Job Now

Human PR vs. AI PR — what changes

Dimension	Human PR	AI PR
Surface polish	Variable	High — formatted, named well, comments present
Test coverage	Often skipped	Usually present (but may be tautological)
Off-by-ones	Common in custom logic	Common in loops AI generated
Hallucinated APIs	Rare	Common — calls to functions that don't exist in your codebase
Architectural fit	Usually consistent	May fight existing conventions
Risk of overconfidence	Self-aware	AI rarely flags its own uncertainty

The structured walk: 7 stops, in order

READ THE COMMIT MESSAGE — does it match the diff? AI-generated messages are often aspirational
READ THE DIFF SUMMARY — count files changed; if >10, push back for a smaller PR
VERIFY IMPORTS — every new import: is it real, is it the right version, is it on the allowlist?
TRACE A REQUEST — pick one user-visible flow and walk it end-to-end through the changes
CHECK EDGE CASES — does empty/null/zero/unicode have a test? Almost never on AI's first pass
RUN THE TESTS LOCALLY — green CI is necessary, not sufficient
READ THE TESTS — are they real tests, or assertions that mirror the implementation?

Five questions to interrogate AI code

# Pin these to your monitor:

1. "What was here before, and why?"
   — AI loves to delete code it doesn't understand the purpose of.

2. "What happens to this function on the most surprising input?"
   — Empty? Null? Unicode? Very large? Concurrent?

3. "Does this change touch any business logic that has documented reasons?"
   — Check ADRs, comments, related issues.

4. "Where in this PR would a malicious user attack?"
   — Even if the feature isn't security-critical, ask.

5. "Could I reproduce this PR's intent in 50% fewer lines?"
   — AI tends toward verbose; cut what doesn't earn its keep.Five questions you ask of every PR. Answers reveal whether the AI understood the task or merely shaped output to look like it did.

The single highest-value review prompt

# Run in a fresh chat with a different model than wrote the PR:

"You are a senior reviewer with no context on this PR's history.
Do not trust the description. Read only the diff.

For each changed file, list:
  1. What this change does (1 sentence)
  2. The single most likely bug introduced
  3. The single most likely regression risk in adjacent code

Do not be polite. Be blunt. List concrete line numbers."Cross-model adversarial review. Catches what same-family review misses.

What to push back on

Renames you didn't ask for — `getUser` -> `fetchUserById` everywhere is a code-churn red flag
New abstractions for one caller — premature generalization
Tests that mirror the implementation (`expect(result).toEqual(result)`)
Changes outside the stated scope of the PR
Comments that explain what the code does, not why

Approving vs. requesting changes

Signal	Action
Imports verified, tests cover edges, scope tight	Approve
Tests are tautological — match implementation 1:1	Request: "rewrite tests against the spec, not the code"
Diff includes unrelated changes	Request: "split into separate PRs"
Hallucinated function call	Request: "verify <function> exists and document its source"
Performance claim with no measurement	Request: "add benchmark or remove the claim"
Security-critical path with no human-written test	Block until written

An AI PR with no review is a commit by an intern who has never seen production.
— An engineering manager

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coding-debug-code-review-workflows-creators

What is the core idea behind "Reviewing AI Code Like a Senior Engineer"?
1. Reviewing AI-written PRs is a different sport from reviewing human ones. Learn the structured review workflow that catches AI-specific bugs, plus the questions that separate confident-looking trash from real engineering.
2. read-only role
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
Which term best describes a foundational idea in "Reviewing AI Code Like a Senior Engineer"?
1. structured walk
2. code review
3. tautological test
4. scope creep
A learner studying Reviewing AI Code Like a Senior Engineer would need to understand which concept?
1. code review
2. tautological test
3. structured walk
4. scope creep
Which of these is directly relevant to Reviewing AI Code Like a Senior Engineer?
1. code review
2. structured walk
3. scope creep
4. tautological test
Which of the following is a key point about Reviewing AI Code Like a Senior Engineer?
1. READ THE COMMIT MESSAGE — does it match the diff? AI-generated messages are often aspirational
2. READ THE DIFF SUMMARY — count files changed; if >10, push back for a smaller PR
3. VERIFY IMPORTS — every new import: is it real, is it the right version, is it on the allowlist?
4. TRACE A REQUEST — pick one user-visible flow and walk it end-to-end through the changes
Which of these does NOT belong in a discussion of Reviewing AI Code Like a Senior Engineer?
1. READ THE DIFF SUMMARY — count files changed; if >10, push back for a smaller PR
2. VERIFY IMPORTS — every new import: is it real, is it the right version, is it on the allowlist?
3. READ THE COMMIT MESSAGE — does it match the diff? AI-generated messages are often aspirational
4. read-only role
Which statement is accurate regarding Reviewing AI Code Like a Senior Engineer?
1. New abstractions for one caller — premature generalization
2. Tests that mirror the implementation (`expect(result).toEqual(result)`)
3. Renames you didn't ask for — `getUser` -> `fetchUserById` everywhere is a code-churn red flag
4. Changes outside the stated scope of the PR
Which of these does NOT belong in a discussion of Reviewing AI Code Like a Senior Engineer?
1. Renames you didn't ask for — `getUser` -> `fetchUserById` everywhere is a code-churn red flag
2. read-only role
3. New abstractions for one caller — premature generalization
4. Tests that mirror the implementation (`expect(result).toEqual(result)`)
What is the key insight about "Use AI to review AI" in the context of Reviewing AI Code Like a Senior Engineer?
1. Tools like Cursor's bug-bot, GitHub Copilot's PR review, and Vercel Agent's code review give a useful second opinion.
2. read-only role
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
What is the key insight about "AI tends to refactor while it's there" in the context of Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. If you ask the agent to fix a bug, it may also rename a variable, reorder imports, and reformat a function.
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
Which statement accurately describes an aspect of Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
3. When AI writes most of your code, reviewing becomes the engineer's primary craft.
4. rubber duck debugging
What does working with Reviewing AI Code Like a Senior Engineer typically involve?
1. read-only role
2. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
3. rubber duck debugging
4. The big idea: AI PRs look better than human ones and fail in different ways.
Which best describes the scope of "Reviewing AI Code Like a Senior Engineer"?
1. It focuses on Reviewing AI-written PRs is a different sport from reviewing human ones. Learn the structured review
2. It is unrelated to ai-coding workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. Human PR vs. AI PR — what changes
3. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
4. rubber duck debugging
Which section heading best belongs in a lesson about Reviewing AI Code Like a Senior Engineer?
1. read-only role
2. Shines: boilerplate, regex, glue code, file format conversion, test scaffolds, r…
3. The structured walk: 7 stops, in order
4. rubber duck debugging

← Back to interactive lesson