How to Code Review AI-Generated Pull Requests Without Burnout
I opened a pull request last week. Three thousand lines of changes. The author had used an AI assistant to implement a simple feature — add validation to a user registration form.
I started scrolling through the diff. Abstract base classes. Factory patterns. Strategy objects. Dependency injection containers. A custom validation framework that could theoretically support seventeen different validation backends.
For a form with four fields.
I closed my laptop. I’d review it tomorrow.
Tomorrow came. I opened the PR again, started at the top, and realized: this is burnout in real-time. Not from coding, but from reviewing AI-generated “solutions” that solve problems we don’t have.
The Problem: AI “Slop” in Pull Requests
A recent Reddit thread captured what many developers feel but rarely say out loud:
“Reviewing PRs full of extremely over engineered slop is exhausting” (611 upvotes)
The comment resonated because it named something we’ve all experienced. AI assistants don’t write bad code — they write excessive code. They mashes together different existing solutions, adding abstraction layers “just in case” and implementing patterns that made sense in Stack Overflow answers but not in your specific context.
The hidden costs are significant:
- Technical debt disguised as completeness: The code looks thorough but creates maintenance burdens
- Knowledge silos: Only the AI (and the person who prompted it) understands why certain patterns were chosen
- Review fatigue: Senior engineers spend cognitive budget decoding AI logic instead of providing valuable feedback
Let me show you what this looks like in practice.
Example 1: The Over-Engineered Validator
Here’s a real pattern I found in an AI-generated PR. The task: validate email and phone number fields.
from abc import ABC, abstractmethodfrom typing import Generic, TypeVar, List, Optionalfrom dataclasses import dataclass
T = TypeVar('T')
@dataclassclass ValidationResult: is_valid: bool errors: List[str]
class Validator(ABC, Generic[T]): @abstractmethod def validate(self, value: T) -> ValidationResult: pass
class EmailValidator(Validator[str]): def __init__(self, max_length: int = 255, allow_plus_sign: bool = True): self.max_length = max_length self.allow_plus_sign = allow_plus_sign
def validate(self, value: str) -> ValidationResult: errors = [] if len(value) > self.max_length: errors.append(f"Email exceeds {self.max_length} characters") if '@' not in value: errors.append("Email must contain @") return ValidationResult(len(errors) == 0, errors)
class PhoneValidator(Validator[str]): def __init__(self, country_code: str = "+1"): self.country_code = country_code
def validate(self, value: str) -> ValidationResult: errors = [] clean = value.replace("-", "").replace(" ", "") if not clean.startswith(self.country_code): errors.append(f"Phone must start with {self.country_code}") return ValidationResult(len(errors) == 0, errors)
class ValidatorFactory: _validators = {}
@classmethod def register(cls, name: str, validator: Validator): cls._validators[name] = validator
@classmethod def get(cls, name: str) -> Optional[Validator]: return cls._validators.get(name)
# UsageValidatorFactory.register('email', EmailValidator())ValidatorFactory.register('phone', PhoneValidator())Eighty lines. For email and phone validation.
When I asked the author about this design, they said the AI suggested it for “extensibility.” But the project had three forms total, and no plans for more.
Here’s what a human would write:
import re
def validate_email(email: str) -> list[str]: errors = [] if len(email) > 255: errors.append("Email exceeds 255 characters") if '@' not in email: errors.append("Email must contain @") return errors
def validate_phone(phone: str) -> list[str]: errors = [] clean = phone.replace("-", "").replace(" ", "") if not clean.startswith("+1"): errors.append("Phone must start with +1") return errorsTwelve lines. Same functionality. No factory. No abstract base classes. No generics.
The over-engineered version isn’t wrong — it’s just solving problems we don’t have. And that’s the pattern to watch for in AI-generated PRs.
Example 2: Unnecessary Abstraction Layers
Another PR I reviewed added configuration for a CLI tool. The AI produced this:
interface ConfigProvider { get(key: string): unknown; has(key: string): boolean;}
class EnvironmentConfigProvider implements ConfigProvider { get(key: string): unknown { return process.env[key]; } has(key: string): boolean { return key in process.env; }}
class JsonFileConfigProvider implements ConfigProvider { private config: Record<string, unknown>;
constructor(path: string) { this.config = require(path); }
get(key: string): unknown { return this.config[key]; } has(key: string): boolean { return key in this.config; }}
class ChainedConfigProvider implements ConfigProvider { private providers: ConfigProvider[];
constructor(...providers: ConfigProvider[]) { this.providers = providers; }
get(key: string): unknown { for (const provider of this.providers) { if (provider.has(key)) { return provider.get(key); } } return undefined; }
has(key: string): boolean { return this.providers.some(p => p.has(key)); }}
// Configuration managerclass ConfigManager { private static instance: ConfigManager; private provider: ConfigProvider;
private constructor() { this.provider = new ChainedConfigProvider( new EnvironmentConfigProvider(), new JsonFileConfigProvider('./config.json') ); }
static getInstance(): ConfigManager { if (!ConfigManager.instance) { ConfigManager.instance = new ConfigManager(); } return ConfigManager.instance; }
get(key: string): unknown { return this.provider.get(key); }}The task was to read three environment variables. That’s it.
const API_URL = process.env.API_URL || 'https://api.example.com';const TIMEOUT = parseInt(process.env.TIMEOUT || '5000', 10);const DEBUG = process.env.DEBUG === 'true';Three lines. The AI version? Over sixty lines with providers, chaining, and a singleton pattern — for reading three variables that would never change their source.
Strategy 1: The Explanation Test
When I encounter over-engineered code in PRs, I use the Explanation Test:
“Explain why this pattern was necessary for this specific use case.”
Not “what does this code do” — but why this approach was chosen over a simpler one.
Here’s how this conversation typically goes:
Me: "I see you've implemented a ValidatorFactory with abstract base classes. Can you walk me through why we need the factory pattern here?"
Author: "The AI suggested it for extensibility."
Me: "What specific extensibility do we need? Do we have plans to add different validation backends?"
Author: "Well, no, not currently."
Me: "So if we only need email and phone validation, would two simple functions work? What would we lose?"
Author: "...Nothing, actually. I think the AI just defaulted to patterns it saw in training data."The Explanation Test surfaces assumptions. AI code makes implicit assumptions about future needs. Making those assumptions explicit reveals whether the complexity is justified.
Strategy 2: The Over-Engineering Audit
I’ve developed a mental checklist for identifying AI artifacts in code reviews:
+----------------------------------------------------------+| OVER-ENGINEERING AUDIT CHECKLIST |+----------------------------------------------------------+| || [ ] ABSTRACT BASE CLASS || Is there only ONE implementation? || → If yes, suspect AI over-abstraction || || [ ] FACTORY PATTERN || Are there < 3 objects being "manufactured"? || → If yes, suspect AI pattern-matching || || [ ] DEPENDENCY INJECTION || Are there < 5 dependencies? || → If yes, suspect AI over-engineering || || [ ] CONFIGURATION PROVIDERS || Is the config source static (env, file)? || → If yes, suspect unnecessary abstraction || || [ ] SINGLETON PATTERN || Is there only ONE caller in the codebase? || → If yes, suspect AI cargo-culting || || [ ] VERBOSE NAMING || Do variable names describe implementation || details rather than intent? || → If yes, suspect AI verbosity || || [ ] HELPER FUNCTIONS || Are there more helpers than main functions? || → If yes, suspect AI decomposition || |+----------------------------------------------------------+When I find 3+ items checked, I ask the author to justify each one. Often, they can’t — and the code gets simpler.
Strategy 3: The Accountability Standard
The hardest part of reviewing AI-generated code is the responsibility gap. The author didn’t write it, the AI did. So who owns the design decisions?
I’ve started requiring this in my teams:
If you submit AI-generated code, you must be able to explain and defend every design decision as if you wrote it yourself.
This isn’t about gatekeeping. It’s about ensuring the person merging the code actually understands it.
A practical implementation:
## PR Checklist for AI-Assisted Code
Before submitting, please confirm:
- [ ] I can explain the "why" behind each major design decision- [ ] I have identified which parts are AI-generated vs human-written- [ ] I have manually tested all code paths, not just the ones I wrote- [ ] I can identify at least one simplification opportunityThe last item is key. AI code almost always has simplification opportunities. If the author can’t find any, they haven’t understood the code well enough to merge it.
Strategy 4: Team Standards for AI-Assisted PRs
After multiple exhausting review cycles, my team established explicit standards:
Before Submitting:
- Run
git diff --stat— if your PR is 5x larger than expected, question the AI’s output - Identify the simplest possible solution — then check if the AI code matches it
- Remove any abstraction that doesn’t have a concrete current use case
During Review:
- Reviewers should comment with “Why this approach?” — authors must provide reasoning, not point to the AI
- “Over-engineered” is valid feedback — the author must simplify, not defend
- Focus on maintainability over theoretical extensibility
After Merge:
- Document AI-generated patterns in code comments if they’re non-obvious
- Schedule a 2-week follow-up: is the code still understandable?
- If multiple PRs show similar AI patterns, add to team’s “avoid” list
Strategy 5: Efficient Review Techniques
When facing a large AI-generated PR, I use this approach:
+----------------------------------------------------------+| REVIEW WORKFLOW |+----------------------------------------------------------+| || 1. SKIM FIRST (5 min) || - Get diff stats: file count, line count || - Identify the "shape" of the change || - Flag suspicious patterns immediately || || 2. FOCUS POINTS || - Entry points (where does code start?) || - Exit points (what does it return?) || - Integration points (how does it connect?) || || 3. READ BACKWARDS || - Start at the test file || - Work back to implementation || - This reveals what SHOULD happen vs what DOES || || 4. ASK, DON'T TELL || - "Why did you choose X?" > "This should be Y" || - Forces author to think through decisions || |+----------------------------------------------------------+This prevents me from getting lost in the details of over-engineered code.
Example 4: Good Feedback for AI-Assisted PRs
Here’s an example of effective review feedback on an AI-generated PR:
## Review: User Registration Validator
### Overall AssessmentThe implementation is significantly more complex than needed for ourcurrent requirements. I see patterns from enterprise frameworks thatdon't match our use case.
### Specific Feedback
**1. ValidatorFactory Pattern**
Question: We have exactly 2 validators (email, phone). What futurevalidators are we expecting that justify a factory?
Suggestion: Consider replacing with direct function calls:- `validateEmail(email: string): string[]`- `validatePhone(phone: string): string[]`
**2. Abstract Base Class**
Question: The `Validator` abstract class has only one method. Whatbenefit does the abstraction provide over a simple function signature?
**3. Test Coverage**
The tests are thorough but test the factory pattern rather than theactual validation logic. Can you add tests for:- Valid emails that should pass- Invalid emails that should fail- Edge cases (empty string, null-like values)
### Action Items
1. Simplify to direct function calls2. Add validation logic tests3. Remove unused extensibility hooks
---
**Note:** I understand this was AI-assisted. Please ensure you canexplain the reasoning for keeping any pattern you choose. I'm happyto discuss trade-offs in our next standup.Notice the tone: curious, not accusatory. The feedback focuses on understanding decisions rather than dictating solutions.
The Meta-Problem: Why This Burns Us Out
Reviewing AI-generated code is exhausting because it reverses the normal review dynamic:
+----------------------------------------------------------+| TRADITIONAL vs AI REVIEW |+----------------------------------------------------------+| || TRADITIONAL REVIEW: || Author thought → Author wrote → Reviewer checks || Mental model: Author explains, Reviewer validates || || AI-ASSISTED REVIEW: || Author prompted → AI wrote → Reviewer decodes || Mental model: Reviewer reverse-engineers AI logic || |+----------------------------------------------------------+In traditional reviews, the author has already thought through the problem. In AI-assisted reviews, the reviewer is often the first person to actually think about why the code is structured this way.
That’s the burnout: we’re not reviewing anymore, we’re archaeologizing code, trying to understand what an AI was “thinking” when it generated it.
Practical Takeaways
If you’re feeling burned out from AI-generated PRs:
- Shift the burden back to authors — they must explain their AI-assisted decisions
- Create explicit standards — make “over-engineered” a valid review concern
- Use the Explanation Test — require justification, not just working code
- Focus on simplification — always ask “what can be removed?”
- Limit your cognitive budget — don’t spend more time reviewing than the author spent prompting
The goal isn’t to reject AI assistance. It’s to ensure that the code we merge is code we understand, can maintain, and actually need.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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