The Role of the Reviewer in the Age of LLM-Generated Code

Not so long ago, writing code was the hard part, and reviewing was the safety net. Today, language models churn out code in seconds, and the review often becomes the first real human check.

This changes the balance of effort. Writing code is fast and cheap, but reviewing it is slow and expensive. The role of the reviewer is shifting in ways that put more responsibility and more strain on the human side of the process.

When Review Was Refinement #

In the past, writing working code took time and effort. You’d step through logic line by line with some data, observe values change in the debugger, and in doing so form a clear understanding of what was going on and what could go wrong. By the time a merge request was opened, the author knew the code and its limitations.

The subsequent review was to refine what had already been thought through and to make sure it would fit into the codebase. The reviewer validated reasoning, which the author could easily explain, and suggested possible improvements. At the same time, questions about the change and related parts of the system were answered, transferring knowledge in the process.

The Shifting Burden of Review #

With LLMs, large blocks of code are generated in little time. Authors often scan the output rather than read every line or fully consider the implications before pushing. This leaves the reviewer as the first person to seriously read and reason about the code.

This also changes how we give feedback. Previously, reviewers could tailor their effort based on the author’s experience. A senior engineer’s code might primarily be checked for architectural clarity, while a junior’s would additionally be scrutinized for common pitfalls. With LLM-generated code, that context is lost. The code wasn’t authored at a consistent level, so the reviewer must check everything, from high-level design to trivial mistakes.

The net effect is awkward. Senior engineers now receive feedback on basics like naming, imports, lint, and variable shadowing, things that feel out of place for their level. As a reviewer, you don’t want to burden an experienced engineer with trivial issues, but ignoring them gradually lowers the quality of the codebase. When experienced engineers who know better submit model-generated code without proper vetting, reviewers face an uncomfortable choice: point out every basic issue or let standards slip.

Instead of sanity checks and knowledge transfer, the review becomes a first verification: does this code actually solve the problem and reflect the intent? And beyond that: are there plausible but wrong patterns, nonexistent APIs, faulty imports, mutable defaults, incomplete renames or untested edge cases?

Model-generated code is also harder to comprehend because it lacks the natural flow or structure a human would choose. Comments are excessive and redundant, less a useful addition and more an explanation of the generation process. They’re something a human would never write. Obvious mistakes like placeholder values, unused variables, or dead code blocks signal that the code wasn’t properly read by the author. The reviewer, however, is expected to put in the effort and make sure the code matches the specification.

The inherent pace mismatch compounds this. Generating code is near instant, but reviewing and understanding it can take hours. This creates pressure to skim quickly to avoid being the bottleneck, or spend the time to fully understand the change, at which point it’s often faster to have written it yourself from scratch. Over time, this imbalance leads to review fatigue and points to why traditional review processes need adjustment.

Making Reviews Sustainable #

Shift the burden back to authors: The most effective adaptation is making self-review non-negotiable. Before submitting, authors should remove LLM artifacts, verbose comments, placeholder variables, unused imports, and unnecessary defensive programming patterns. The code should be tailored for human understanding, not just functional. This means taking time upfront to plan the approach rather than accepting the model’s first assumptions, and validating that the solution is actually performant and robust. Lastly, authors should summarize the intent of the change in a way that is easy to understand and review and what parts to pay particular attention to.

Write meaningful tests: LLMs can generate tests quickly, but tests generated in bulk often miss edge cases or test implementation details rather than behavior. The author’s job is to ensure tests are meaningful, no matter if generated by the model or not, they should cover actual edge cases, validate the contract rather than the implementation, and fail for the right reasons. A well tested change makes review faster and builds confidence, but only if the tests themselves were thoughtfully validated.

Keep changes small and focused: Large LLM-generated diffs are exponentially harder to review. Smaller changes make it easier to spot issues and reason about correctness. They also make it clearer when a reviewer’s concerns require iteration versus acceptance.

Automate the trivial: Linters, formatters, and static analysis should catch surface-level issues before human review begins. This frees reviewers to focus on logic, architecture, and correctness rather than style and imports.

Use LLMs to assist review: LLMs can help reviewers understand unfamiliar code, suggest edge cases, or explain complex logic. Use them to speed up comprehension and generate questions, but the final judgment on correctness and maintainability remains human. Automated review tools that detect common LLM patterns or flag high-risk sections can also help. These tools are still maturing, but they can catch common issues early or challenge the reviewer to think more deeply about certain code changes.

Establish clear expectations: Teams need explicit standards for what review-ready code looks like. This includes passing automated checks, demonstrable manual testing, and evidence that the author has verified the solution themselves, using screenshots, screen recordings, or other evidence. Teams should also distinguish between assistance levels since there’s a difference between “I wrote this with LLM assistance” and “I generated this with an LLM.” The latter should not be treated as review-ready code.

Conclusion #

The cost balance has flipped: writing code is cheaper than ever, reviewing it is increasingly expensive. Human time is shifting from typing to reading, validating, and making sure things actually work.

LLMs are powerful tools that can boost productivity significantly. But maintaining quality at a sustainable pace means adjusting how we use them. The workflow is changing—we write specifications, LLMs handle implementation, and humans ensure correctness. In this world, lines of code or features shipped matter less than clear intent and thorough self-review.

This will keep evolving. Automated reviews are getting better, but the fundamental problem stays the same: someone has to actually understand and own the code. Teams that build strong review culture now, before bad habits stick, will be the ones who maintain quality as AI-generated code becomes the norm.

The reviewer’s role is more critical than ever. Not just catching bugs, but making sure the code we ship is something we can actually understand, maintain, and trust. That means knowing where human judgment can’t be replaced, and making sure authors take responsibility for what they submit, not just what they generate.