How to Review AI-Generated Python Code Efficiently

Prerequisites

To follow along, you should be comfortable reading Python code and working with a coding agent, such as Claude Code, GitHub Copilot CLI, Antigravity CLI, and OpenCode. You’ll also get more from it if you’re already familiar with debugging Python errors, type checking, and reading a Git diff.

Ideally, you’ll want to practice with some AI-generated code. You’ll learn the most when you run the workflow on a real agent pull request or a small project that an agent has generated. Use the Python version that the target project uses. For a new project, you can choose a recent one. Python 3.14 is a good choice.

You’ll also need some tools to run quality checks on your code, such as linters, static type checkers, security scanners, and test runners. You can install them into your project’s virtual environment. Here’s a minimal set of tools you can use:

(venv) $ python-mpipinstallruffmypybanditpytestpytest-covpip-audit

These are development dependencies, so they should stay separate from the packages your project needs to run. If you manage your project with uv, then you can add them to a dedicated dev dependency group instead:

$ uvadd--devruffmypybanditpytestpytest-covpip-audit

If you prefer a faster type checker, swap mypy for ty, a newer alternative from the makers of Ruff.

For the deeper maintainability checks, add the extended set when you need it:

(venv) $ python-mpipinstallpylintradonimport-linterpydeps

You can configure the core tools in one place. This minimal configuration turns on the rules that matter most for AI-generated code:

# ...

[tool.ruff.lint]
select=["F","E","B","S","UP","DTZ"]

[tool.mypy]
strict=true

[tool.coverage.run]
branch=true

You’ll also want a way to see what changed in the code. Most code reviews happen on a GitHub pull request (PR). There, you can read the diff in the browser with a friendly user interface (UI).

You can also read the diff locally in your terminal. The GitHub CLI tool prints a pull request’s changes with gh pr diff, and when there’s no PR yet, Git shows them with git diff.

You might also be able to review the diff inside your favorite AI coding agent. Tools like Claude Code, GitHub Copilot CLI, and Antigravity CLI have a /diff slash command for this task.

Finally, download the cheat sheet for reviewing AI-generated code. It condenses the mistakes you’ll hunt for into a single page, so you can keep it handy as you follow along now and reuse it in your own reviews later:

Set the Ground Rules to Review AI-Generated Code

Five principles guide the proposed review workflow. They’ll keep you from trusting generated code just because it looks right:

1. You own the result, no matter who wrote it: You, as the human reviewer or developer, are the one who confirms correctness, security, and maintainability. The agent is a helper, not a substitute, and won’t take responsibility for failed code.
2. Reviewing AI-generated code is hands-on: Unlike with a teammate’s pull request, there’s no separate author to send the review comments back to, so you’ll often make the fix yourself instead of just leaving comments. You can prompt the agent for some issues and fix others manually.
3. Plausible isn’t the same as correct: Modern agents can write code that looks right but may be logically wrong, insecure, or unmaintainable.
4. You review against the code’s intent: You check the code against what it’s supposed to do, not just whether it runs or passes tests. The agent can misread the prompts and write code that looks fine but doesn’t meet the requirements.
5. Automated code-quality checks are necessary but never sufficient: Linters, type checkers, security scanners, and tests clear the mechanical issues, but they miss logic bugs and bad abstractions. Running them handles the routine noise, so you can focus on the problems that matter.

These principles are the mindset behind the review workflow, not a checklist you tick off. They don’t map one-to-one onto the steps that follow. Instead, each principle shows up across several of them.

This workflow has five steps:

1. Understand What the Code Should Do: Work out the goal before reading the code, so you judge the code against intent rather than plausibility.
2. Run Automated Code-Quality Checks: Use linters, a type checker, a security scanner, and tests to clear the mechanical issues.
3. Read With a Plan, Not Top to Bottom: Read what’s left risk-first and outside-in, following the code’s flow.
4. Hunt for Common Issues in AI-Generated Code: Check the things that AI agents normally get wrong, then confirm each flag.
5. Fix the Issues, Then Verify the Fix: Fix what you confirmed, then rerun to verify the fix holds.

Steps 1, 2, 3, and 5 include practices that you commonly use when doing a code review for a teammate. However, the workflow accounts for the presence of an active AI coding agent. Step 4 is where you do the AI-specific checks. Now, it’s time to explore each step in order, starting with the code’s goal.

Step 1: Understand What the Code Should Do

A quick way to spot a bad diff is to know what a correct one should look like. In an AI-assisted coding environment, a common failure looks like this: the agent misreads your prompt and context, then implements that misreading flawlessly. The syntax will be fine, but the code can take an unexpected path. As with a human-generated diff, your review starts before the code.

First, figure out the change’s intent. To this end, you can use some of the following sources, depending on what triggered the code diff:

• An issue ticket or bug report
• A feature specification
• A failing test (red-green cycle)
• An acceptance criterion
• A commit message or pull request description
• A prompt you gave the agent or a conversation history

Once you have a clear idea of what the code should do, keep that intent in front of you. You’ll check the code against that intent rather than whether it looks reasonable.

When you review a new AI-generated project, you’ll have no diff to start with. You can gather the code’s intent from the project specification, the agent’s implementation plan, the prompts you originally gave the agent, and so on. In this case, you should review the project by modules or packages, one piece at a time. Working in small, logical chunks keeps your accuracy high and stops you from getting lost in a big wall of code.

With a clear idea of the code’s intent, you have a good starting point to check its correctness. However, before you read a single line, you should run some automated checks to make sure the code is clean.

Step 2: Run Automated Code-Quality Checks

Linters, static type checkers, security scanners, and test runners are fast and deterministic. They help you catch boring and repetitive issues so you can focus on the important parts. Let them clear the mechanical noise for you first.

Here are four tools you can run as a first pass:

• ruff as a linter, including the F rules that flag undefined or unused names
• mypy in strict mode to catch type mismatches and impossible calls
• bandit as a security scanner for issues like hardcoded secrets and unsafe defaults
• pytest with branch coverage, which flags untested error cases that happy-path-only code tends to skip

For example, with the pyproject.toml file from the prerequisites section in place, one hardcoded key that the agent left behind gets caught right away:

# ...

SECRET_KEY = "sk-live-1234567890abcdef"

Ruff reports the key as a hardcoded secret through its S rules, and Bandit catches it with its default settings. You can fix this security issue by reading the key from an environment variable or a .env file.

A few more checks could help with AI-generated code:

• Dependency audit: Use a tool like pip-audit to catch dependencies with known vulnerabilities.
• Outdated constructs: Use Ruff’s UP and DTZ rules to flag potentially dated patterns like datetime.utcnow() or typing.List that models regurgitate from their training data.
• Code duplication check: Use pylint and its duplicate-code check to catch the same block of code pasted in multiple places. Pass all the modules in a single run, since the check only compares the files that it sees together.
• Code complexity check: Use a tool like radon to measure how complex your code is.

These are just a sample. There are many more linters and checkers out there, so pick the ones that fit your project and stack instead of running them all blindly.

If you later ask the agent to fix the issues, then you must still confirm that the fixes hold. The agent can misread the checker’s output and apply a fix that doesn’t actually improve the code. For example, some code blocks can be intentionally duplicated for performance reasons, and a hardcoded secret can be a deliberate test key.

Running linters and checkers is necessary but not sufficient. The next step is where you read what’s left.

Step 3: Read With a Plan, Not Top to Bottom

Now it’s time for you to read the code. However, don’t do a top-to-bottom pass through the diff in the order it’s listed. Instead, you should look at the risky parts first. For example, you can start with the code that handles the following aspects, roughly in this order:

• Security: Missing authorization checks, hardcoded secrets, and unauthorized access are the kinds of flaws that end in data breaches, account takeovers, and full system compromise.
• Input validation: Unsanitized user input, missing type or length checks, and unescaped query parameters can lead to code injection attacks, application crashes, and silent data corruption.
• Data handling: Unsafe deserialization of untrusted data, logging user credentials in plaintext, and leaky error messages can cause data exposure, compliance violations, and remote code execution.
• Control flow: A missing early return or break, a never-changing condition, or an unhandled error path can cause incorrect results, inconsistent state, and hard-to-debug behavior.
• Public interfaces: Changing a function’s signature or return type, renaming public methods, or exposing internal details can break client code, cause downstream outages, and force user rewrites.

Overall, you should read outside-in, following the code’s flow rather than the diff order. First, get a general idea of the change from its entry points and structure. Only then read the internal line-level details.

If you’re reviewing a freshly generated project, then you have no history to diff against. So, use the module layout and the import arrangement to check the structure first. Then, dive into the modules and packages by relevance, according to the project’s intent.

So far, you’ve looked at the review strategy, which is how you move through the code or diff. Next, you need specific targets to make the review efficient. In human-generated code, bugs are scattered and hard to predict, which makes your job harder. In AI-generated code, the issues fall into a few predictable types, which makes your job easier. You can turn those issue types into a short checklist that guides your review.

Make sure you’ve downloaded the cheat sheet for reviewing AI-generated code, which gathers those mistakes into a single page you can keep beside you as you work through this tutorial—and on every review afterward.

Step 4: Hunt for Common Issues in AI-Generated Code

This step is where reviewing AI-generated code differs from reviewing a teammate’s pull request. The types of issues you’ll find may not be new, but the mix is. Also, the issues tend to be more predictable than the ones you find in human-written code. Remember, AI is pretty good at making things look plausible, but that doesn’t mean they’re correct.

In the following sections, you’ll explore some common issues in AI-generated code, from high-level to low-level. First, you’ll check whether the change fits into the codebase structure. Next, you’ll look at bugs that break things. Finally, you’ll see how problems can build up over time.

# ...

classHelper:
    defparse_csv(self, path):
"""Parse a CSV file using the csv module."""
        # Implementation here...

    defsend_email(self, to, subject):
"""Send an email using the smtplib module."""
        # Implementation here...

    defresize_image(self, path, width):
"""Resize an image using the Pillow library."""
        # Implementation here...

>>> defmoving_averages(values, window):
... """Return the average of each sliding window over the values."""
...     averages = []
...     for start in range(len(values) - window):
...         averages.append(sum(values[start : start + window]) / window)
...     return averages
...

>>> moving_averages([1, 2, 3, 4], 2)
[1.5, 2.5]

try:
    config = load_config(path)
except Exception:
    config = {}

deffind_user(cursor, username):
    cursor.execute(
        f"SELECT * FROM users WHERE name = '{username}'"
    )
    return cursor.fetchone()

importpathlib

settings = pathlib.Path("config.toml").read_toml()

deforder_totals(connection, order_ids):
"""Return the total for each of the given order IDs."""
    totals = []
    for order_id in order_ids:
        cursor = connection.execute(
            "SELECT total FROM orders WHERE id = ?", (order_id,)
        )
        totals.append(cursor.fetchone()[0])
    return totals

importrequests

async deffetch_prices(symbols):
"""Return the current price for each of the given symbols."""
    prices = {}
    for symbol in symbols:
        response = requests.get(f"https://api.example.com/price/{symbol}")
        prices[symbol] = response.json()["price"]
    return prices

defprocess(data):
    return [row for row in data if row.is_active]

Step 5: Fix the Issues, Then Verify the Fix

Now you have a list of common issues. Work through them one at a time and look into each fix before you apply it. Re-prompt the agent for small or mechanical gaps, like a missing edge case or an outdated construct.

Structural issues, like a wrong abstraction, take judgment and context that the agent may not have. You can fix those yourself, or you can ask the agent to propose a fix and then confirm it.

Run the code and the full set of tests to confirm the fix works and didn’t break anything nearby. For a whole generated project, run it from scratch because there’s no earlier working state to rely on.

Depending on the project’s internal rules, you might not fix the issues yourself. In that case, you can report the issues as review comments for another human developer to handle, with or without an agent’s help.

Troubleshooting

A few problems come up often, even once you know the workflow. None of them mean you’re doing it wrong. They’re just part of reviewing this much code this fast, and each one has a quick fix:

• The automated checks produce too much output: Ask the agent to apply fixes one by one in a loop until everything is cleared out.
• The agent keeps missing the point: Clear the conversation or start a fresh session with a better prompt and context. Alternatively, you can fix the issue yourself and then ask the agent to confirm it.
• The amount of code overwhelms you: Take a break, and when you’re back, split the code into smaller chunks. Use the agent as a helper tool. You’re not a machine, and your accuracy drops when you try to read too much at once.

The thread through all three is the same: don’t let the pace push you into skimming the code or accepting changes blindly. When the review gets away from you, shrink the problem, slow down, and stay the one who decides what’s actually fixed.

Next Steps

You now have a repeatable workflow for reviewing AI-generated code. From here, you can go deeper on the parts that matter most for your project and team:

• Get better at writing high-quality Python code.
• Learn more about Python’s exceptions for error handling, the with statement for avoiding resource leaks, and the datetime module for correct date math.
• Master AI-assisted coding with the AI coding agents guide and specific agents like Claude Code, GitHub Copilot CLI, Antigravity CLI, and OpenCode.
• Learn how to manage the agent’s context window effectively, so you can improve the generated code.
• Explore the other side of the workflow with GitHub Copilot’s automated code review, where an AI agent reviews the pull request for you.
• Follow a structured curriculum with Real Python’s Python Coding With AI learning path, which gathers tutorials and video courses on coding with AI tools into one guided track.

Reviewing AI-generated code effectively is how you keep the productivity and augmented capabilities that agents give you without compromising your codebase’s future. By following and fine-tuning this workflow, you’ll be able to ship high-quality code faster and with confidence.

Frequently Asked Questions

Now that you have a workflow for reviewing AI-generated code in Python, you can use the questions and answers below to check your understanding and recap what you’ve learned.

These FAQs are related to the most important concepts you’ve covered in this tutorial. Click the Show/Hide toggle beside each question to reveal the answer.