Checklist for Reviewing LLM-Generated PRs (Deep Technical Guide – 2026)

LLM-generated pull requests are fundamentally different from human-written ones.

They are:

• Fast
• Fluent
• Confident
• Often plausible

But plausibility is not correctness.

This checklist is designed for serious engineering teams reviewing AI-generated PRs in production environments. It goes beyond linting, formatting, or surface-level review.

Category 1: Intent Alignment & Requirement Drift

1.1 Does the PR actually solve the original requirement?

LLMs frequently over-interpret prompts. Before reviewing code quality, verify:

• Does the diff match the original objective?
• Did the AI introduce extra features?
• Did it remove existing behavior unintentionally?

AI often expands scope silently.

1.2 Has business logic subtly changed?

Look for:

• Changed conditional ordering
• Modified default return values
• Implicit type conversions
• Shifted edge-case handling

LLMs may "simplify" logic in ways that alter business outcomes.

Category 2: Authentication & Authorization Enforcement

2.1 Is authentication middleware present?

Check:

• Every new route
• Every modified endpoint
• Every resolver or controller

AI often implements functionality without enforcing access control.

2.2 Are role checks explicit?

• No implicit trust of user objects
• No skipped admin validation
• No soft "if (user)" checks without role validation

2.3 Is multi-tenant isolation preserved?

• Are queries scoped by tenant ID?
• Is cross-account data exposure prevented?

Category 3: Hallucinated Dependencies & Assumptions

3.1 Do all imported modules exist?

• Check package.json
• Verify internal utilities
• Confirm function implementations

3.2 Are referenced database fields real?

• Schema consistency
• Migration alignment
• No assumed properties

3.3 Are helper functions actually secure?

AI frequently generates functions with names like:

validateAccess()
sanitizeInput()
secureQuery()

Verify their implementation — not just their name.

Category 4: Cloud Cost & Infrastructure Risk

4.1 Are queries bounded?

• Missing pagination?
• Unbounded fetches?
• Full-table scans?

4.2 Are IAM roles minimally scoped?

• No wildcard permissions
• No unnecessary service access

4.3 Are cloud defaults secure?

• Public buckets disabled?
• Logging sanitized?
• Secrets stored correctly?

Category 5: Edge Cases & Failure States

5.1 What happens on null input?

5.2 What happens on empty state?

5.3 What happens under concurrency?

5.4 What happens if upstream service fails?

LLMs optimize for happy path. Your review must test adversarial paths.

Category 6: Regression Risk

6.1 Did the PR modify shared utilities?

6.2 Did it alter core middleware?

6.3 Did it refactor critical flows?

LLMs often refactor aggressively. This increases regression surface area.

Category 7: Logging & Data Exposure

• PII in logs?
• Tokens exposed in debug output?
• Internal error messages leaking system details?

Verbose AI logging must be sanitized before merge.

Category 8: Test Coverage Integrity

8.1 Are new code paths covered?

8.2 Are only success paths tested?

8.3 Do tests mirror implementation too closely?

AI-generated tests frequently validate the same flawed assumptions as the code.

Category 9: Determinism & Version Drift

Ask:

If we regenerate this feature tomorrow, would the output differ materially?

Non-determinism can create unstable behavior across deployments.

Category 10: Merge Readiness

• Requirement alignment validated
• Auth enforced
• No hallucinated imports
• Cloud cost impact assessed
• Edge cases handled
• Regression risk evaluated
• Logging sanitized
• Tests strengthened

Only then should the PR move forward.

Why Manual Review Alone Doesn’t Scale

As AI-generated PR volume increases, manual enforcement of this checklist becomes difficult.

Teams need automated AI-specific PR diff analysis to:

• Detect hallucinated dependencies
• Flag missing enforcement logic
• Identify cost-heavy patterns
• Highlight regression risk

Tools like Codebase X-Ray are designed specifically for AI-generated PR verification.

Run 3 free PR scans at prodmoh.com.

Final Principle

AI-generated code is not inherently insecure.

But it fails differently than human-written code.

Your review process must evolve accordingly.

Fluent output is not production-ready output.