Pull Requests That Don't Waste Your Time

Pull Requests That Don't Waste Your Time

November 3, 2025

Solo developers and small teams struggle with PR overhead. Here's how to optimize PRs for speed and quality—and why AI review requires different thinking than AI code generation.

Pull Requests That Don't Waste Your Time

You just spent three hours implementing a feature. Now you're staring at the "Create Pull Request" button, weighing whether the overhead is worth it.

For solo developers, PRs feel like process theater. You're reviewing your own code. You're approving your own work. The entire workflow was designed for teams with external reviewers, not for someone working alone at 11 PM on a side project.

But here's what changed: AI code review tools now offer a legitimate second pair of eyes for solo developers. The catch? These same AI tools that help you write code faster are also making codebases less stable. Unless you change how you work.

The question isn't whether to use PRs. It's how to use them to catch the problems AI-assisted development introduces, without process overhead killing your momentum.

The size paradox

Here's something counterintuitive from analyzing pull request patterns: teams that consistently ship smaller PRs end up shipping more total code than teams that batch changes into large PRs.

Not more code per PR. More code overall.

Smaller PRs merge faster because they're easier to review. They get caught in "waiting for review" state less often. They have lower revert rates because focused changes are easier to test thoroughly. And they generate more substantive feedback—when reviewers see 50 lines, they actually review. When they see 500 lines, they skim.

The practical sweet spot is 25-100 lines per PR. Beyond 400 lines, effective review becomes nearly impossible—whether you're reviewing manually or using AI tools.

Why does size matter specifically for AI code review?

AI review tools excel at pattern matching and anomaly detection. Feed them a 500-line PR that mixes refactoring, feature work, and bug fixes, and the signal-to-noise ratio collapses. They'll catch syntax errors but miss the architectural problems buried in the complexity.

Feed them a focused 50-line feature addition, and they can analyze control flow, identify edge cases you missed, and suggest optimizations specific to that exact problem.

The insight: Small PRs don't slow development. They accelerate it by enabling higher-quality feedback loops. Human or AI.

When PRs are worth it (and when they're not)

Let's be honest about the tradeoffs.

Many PRs receive zero substantive comments. Either the code is obviously correct, the reviewer is rubber-stamping, or it's an automated merge. For solo developers, that last category is common. You're not getting fresh eyes. You're getting documentation, CI/CD automation, and potentially AI analysis.

That's still valuable, but not for everything.

PRs make sense when:

  • The change is complex enough that a 10-minute review saves a 60-minute debugging session later
  • You're touching critical paths (authentication, payment, data deletion)
  • The feature needs documentation anyway
  • CI/CD checks provide value (tests, linting, security scans)
  • You want AI review to catch patterns you habitually miss

PRs waste time when:

  • Fixing a typo in documentation
  • Updating dependencies with no code changes
  • Making tiny CSS tweaks that need rapid iteration
  • Working on throwaway prototype code

We've seen developers create PRs for literal one-line changes because "that's the process." But process without value is waste.

If reviewing the change takes longer than making the change, skip the PR. If the consequences of bugs outweigh the overhead, create the PR.

The AI generation problem (and why AI review is different)

Here's the truth about AI-assisted coding: it's making some codebases worse, not better.

A 2024 Uplevel study tracking 800 developers found that those using GitHub Copilot introduced 41% more bugs than their peers. Google's DORA research shows that every 25% increase in AI adoption correlates with a 7.2% reduction in delivery stability.

Developers treat AI as authority rather than assistant. When Copilot suggests code, it arrives with the confidence of autocomplete. No uncertainty markers. No caveats. This triggers a cognitive shortcut: "the machine suggested it, so it must be right."

AI makes writing code feel effortless, so developers write more code without thinking more. The constraint that forced you to pause—typing—is gone. You can generate a hundred lines in seconds. That psychological speed removes the natural moment of reflection.

Developers review AI-generated code less critically than human-written code. When a colleague writes code, you question it. When AI writes code, many developers just verify it runs. The code looks professional, so it passes the sniff test even when it shouldn't.

Why AI review works differently

AI code review doesn't have these problems because the incentive structure is inverted.

When AI generates code, you want to accept it quickly and move forward. Speed is the entire point. When AI reviews code, you're not in a rush. You're in quality-check mode. Skepticism is built into the process.

This creates a natural check-and-balance: AI generation optimizes for velocity, AI review optimizes for correctness. They work in opposite directions, which is exactly what you need.

But this only works if you structure your workflow correctly.

Optimizing PRs for AI review

AI code review tools need three things to be useful: context, constraints, and clear success criteria.

Structure your PR description for machines, not just humans:

## Context
Implementing user profile search with fuzzy matching

## Security considerations
- User input is sanitized before Postgres query
- Rate limiting: 10 requests/minute per user
- Results exclude soft-deleted users

## Edge cases tested
- Empty search queries
- Special characters in names
- Unicode/emoji in profiles
- Pagination with deleted users

This isn't for you. This is explicitly telling the AI what to look for. Generic descriptions like "Add search feature" generate generic feedback. Specific context generates specific analysis.

Keep PRs focused and single-purpose. AI tools struggle with mixed intent. A PR titled "Update user service" that includes refactoring, new features, and bug fixes confuses pattern analysis. Three separate PRs, each optimized for its specific change type, produce dramatically better feedback.

Use feature flags for incomplete work. Don't batch five related changes into one massive PR. Ship incrementally behind flags. Each small PR gets thorough AI review. The big-batch approach gets skimmed—by humans and AI alike.

The honest assessment

Will optimized PRs with AI review transform your development workflow?

For some developers, absolutely. If you're working on side projects without code review, AI fills a legitimate gap. If you're a small team shipping frequently, the cost per review makes financial sense. If you're using AI code generation heavily (Copilot, Cursor, etc.), AI review acts as a necessary counterbalance.

For others, not really. If your code is simple CRUD without edge cases, AI won't find much. If you're already extremely disciplined about self-review, the marginal improvement may not justify the workflow change. If you're working on throwaway prototypes, skip the ceremony.

Here's the nuance that matters: AI review is most valuable when you're using AI generation.

If you're writing every line manually, pausing to think between each function, your code already has the benefit of human deliberation. AI review might catch a few things, but you're not in the high-risk category.

If you're accepting Copilot suggestions rapidly, generating boilerplate with ChatGPT, or using Cursor to scaffold entire features, you're in the danger zone. You're optimizing for speed over correctness. AI review becomes your safety net.

The value proposition isn't "AI makes code review better." It's "AI code generation creates new risks, and AI code review is the least expensive way to mitigate them."

What we're building

We built CodeReviewr because we kept paying $30/month for code review tools we used twice a week. The per-seat model didn't match our usage pattern. So we made usage-based pricing work: no subscriptions, no seat licenses, just pay-per-token pricing.

But the valuable insight isn't "use our product." It's structure changes for review before you need review.

Keep PRs under 100 lines when possible. Write descriptions that explain the why, not just the what. Add explicit context about edge cases and security considerations. Be more skeptical of AI-generated code than hand-written code.

Do that consistently, and whether you use AI review (CodeReviewr, CodeRabbit, Qodo), human review, or just disciplined self-review, your code quality improves. The discipline matters more than the tooling.

Here's to shipping quality code without wasting time on process theater.

If usage-based pricing appeals to you, try CodeReviewr. If you have thoughts on what makes AI code review actually useful versus performative, we're listening at [email protected].