What does AI Code look like?

What does AI Code look like?

Short answer: AI-assisted code often reads as unusually tidy and “textbook”: consistent formatting, generic naming, polite error messages, and comments that restate the obvious. If it’s missing real-world grit - domain language, awkward constraints, edge cases - it’s a warning sign. When you anchor it in your repo patterns and test it against production risks, it becomes trustworthy.

Key takeaways:

Context check: If domain terms, data shapes, and constraints aren’t reflected, treat it as risky.

Over-polish: Excessive docstrings, uniform structure, and bland names can signal generic generation.

Error discipline: Watch for broad exception catches, swallowed failures, and vague logging.

Abstraction trim: Delete speculative helpers and layers until only the smallest correct version remains.

Reality tests: Add integration and edge-case tests; they expose “clean world” assumptions fast.

What does AI Code look like? Infographic

AI-assisted coding is everywhere now (Stack Overflow Developer Survey 2025; GitHub Octoverse (Oct 28, 2025)). Sometimes it’s superb and saves you an afternoon. Other times it’s… suspiciously polished, a bit generic, or it “works” until someone clicks the one button nobody tested 🙃. That leads to the question people keep raising in code reviews, interviews, and private DMs:

What AI Code tends to look like

The direct answer is: it can look like anything. But there are patterns - soft signals, not courtroom evidence. Think of it like guessing whether a cake came from a bakery or someone’s kitchen. The frosting might be too perfect, but also some home bakers are just terrifyingly good. Same vibe.

Below is a practical guide for recognizing common AI fingerprints, understanding why they happen, and - importantly - how to turn AI-generated code into code you’d trust in production ✅.

🔗 How does AI predict trends?
Explains pattern learning, signals, and forecasting in real use.

🔗 How does AI detect anomalies?
Covers outlier detection methods and common business applications.

🔗 How much water does AI use?
Breaks down data-center water use and training impacts.

🔗 What is AI bias?
Defines bias sources, harms, and practical ways to reduce it.

1) First, what people mean when they say “AI code” 🤔

When most folks say “AI code,” they usually mean one of these:

  • Code drafted by an AI assistant from a prompt (feature, bugfix, refactor).

  • Code heavily completed by autocomplete, where the developer nudged but didn’t fully author.

  • Code rewritten by AI for “cleanup,” “performance,” or “style.”

  • Code that looks like it came from an AI even if it didn’t (this happens more than people admit).

And here’s a key point: AI doesn’t have a single style. It has tendencies. A lot of those tendencies come from trying to be broadly correct, broadly readable, and broadly safe… which can ironically make the output feel a bit samey.

2) What AI Code tends to look like: the quick visual tells 👀

Let’s answer the headline plainly: What AI Code tends to look like.

Often it looks like code that is:

  • Very “textbook tidy” - consistent indentation, consistent formatting, consistent everything.

  • Verbose in a neutral way - lots of “helpful” comments that don’t help much.

  • Over-generalized - built to handle ten imaginary scenarios instead of the two real ones.

  • Slightly over-structured - extra helper functions, extra layers, extra abstraction… like packing for a weekend trip with three suitcases 🧳.

  • Missing the awkward edge-case glue that real systems accumulate (feature flags, legacy quirks, inconvenient constraints) (Martin Fowler: Feature Toggles).

But also - and I’ll keep repeating this because it matters - human developers can absolutely write like this too. Some teams enforce it. Some people are just neat freaks. I say that with love 😅.

So instead of “spotting AI,” it’s better to ask: does this code behave like it was written with real context? Context is where AI often slips.

3) The “uncanny valley” signs - when it’s too neat 😬

AI-generated code often has a certain “gloss.” Not always, but often.

Common “too neat” signals

  • Every function has a docstring even when it’s obvious.

  • All variables have polite names like result, data, items, payload, responseData.

  • Consistent error messages that sound like a manual: “An error occurred while processing the request.”

  • Uniform patterns across unrelated modules, like everything was written by the same careful librarian.

The subtle giveaway

AI code can feel like it was designed for a tutorial, not a product. It’s like… wearing a suit to paint a fence. Very proper, slightly wrong activity for the outfit.

4) What makes a good version of AI code? ✅

Let’s flip it. Because the goal isn’t “catch AI,” it’s “ship quality.”

A good version of AI-assisted code is:

In other words, great AI code looks like… your team wrote it. Or at least, your team adopted it properly. Like a rescue dog that now knows where the couch is 🐶.

5) The pattern library: classic AI fingerprints (and why they happen) 🧩

Here are patterns I’ve seen repeatedly in AI-assisted codebases - including ones I’ve personally cleaned up. Some of these are fine. Some are dangerous. Most are just… signals.

A) Over-defensive null checking everywhere

You’ll see layers of:

  • if x is None: return ...

  • try/except Exception

  • multiple fallback defaults

Why: AI tries to avoid runtime errors broadly.
Risk: It can hide real failures and make debugging gross.

B) Generic helper functions that don’t earn their existence

Like:

  • process_data()

  • handle_request()

  • validate_input()

Why: abstraction feels “professional.”
Risk: you end up with functions that do everything and explain nothing.

C) Comments that restate the code

Example energy:

  • “Increment i by 1”

  • “Return the response”

Why: AI was trained to be explanatory.
Risk: comments rot fast and create noise.

D) Inconsistent depth of detail

One part is super detailed, another part is mysteriously vague.

Why: prompt focus drift… or partial context.
Risk: weak spots hide in the vague zones.

E) Suspiciously symmetric structure

Everything follows the same skeleton, even when business logic shouldn’t.

Why: AI likes repeating proven shapes.
Risk: requirements aren’t symmetric - they’re lumpy, like badly packed groceries 🍅📦.

6) Comparison Table - ways to evaluate what AI Code tends to look like 🧪

Below is a practical toolkit comparison. Not “AI detectors,” more like code reality checks. Because the best way to identify questionable code is to test it, review it, and observe it under pressure.

Tool / Approach Best for (audience) Price Why it works (and a tiny quirk)
Code Review Checklist 📝 Teams, leads, seniors Free Forces “why” questions; catches generic patterns… sometimes feels nitpicky (Google Engineering Practices: Code Review)
Unit + Integration Tests ✅ Everyone shipping features Free-ish Reveals missing edge cases; AI code often lacks in-production fixtures (Software Engineering at Google: Unit Testing; The Practical Test Pyramid)
Static Analysis / Linting 🔍 Teams with standards Free / Paid Flags inconsistencies; won’t catch “wrong idea” bugs though (ESLint Docs; GitHub CodeQL code scanning)
Type Checking (where applicable) 🧷 Larger codebases Free / Paid Exposes vague data shapes; can be annoying but worth it (TypeScript: Static Type Checking; mypy documentation)
Threat Modeling / Abuse Cases 🛡️ Security-minded teams Free AI may ignore adversarial use; this forces it into the light (OWASP Threat Modeling Cheat Sheet)
Performance Profiling ⏱️ Backend, data-heavy work Free / Paid AI can add extra loops, conversions, allocations - profiling doesn’t lie (Python docs: The Python Profilers)
Domain-Focused Test Data 🧾 Product + engineering Free The fastest “smell test”; fake data makes fake confidence (pytest fixtures docs)
Pair Review / Walkthrough 👥 Mentoring + critical PRs Free Ask author to explain choices; AI-ish code often lacks a story (Software Engineering at Google: Code Review)

Yeah the “Price” column is a little goofy - because the expensive part is usually attention, not tooling. Attention costs… everything 😵💫.

7) Structural clues in AI-assisted code 🧱

If you want the deeper answer to what AI Code tends to look like, zoom out and look at structure.

1) Naming that’s technically correct but culturally wrong

AI tends to pick names that are “safe” across many projects. But teams develop their own dialect:

  • You call it AccountId, the AI calls it userId.

  • You call it LedgerEntry, the AI calls it transaction.

  • You call it FeatureGate, it calls it configFlag.

None of this is “bad,” but it’s a hint the author didn’t live inside your domain for long.

2) Repetition without reuse, or reuse without reason

AI sometimes:

  • repeats similar logic in multiple places because it doesn’t “remember” the whole repo context in one go, or

  • forces reuse through abstractions that save three lines but cost three hours later.

That’s the trade: less typing now, more thinking later. And I’m not always sure that’s a good trade, I guess… depends on the week 😮💨.

3) “Perfect” modularity that ignores real boundaries

You’ll see code split into neat modules:

  • validators/

  • services/

  • handlers/

  • utils/

But the boundaries might not match your system’s seams. A human tends to mirror the architecture’s pain points. AI tends to mirror a tidy diagram.

8) Error handling - where AI code gets… slippery 🧼

Error handling is one of the biggest tells, because it requires judgment, not just correctness.

Patterns to watch

What good looks like

A very human trait is writing an error message that’s slightly annoyed. Not always, but you know it when you see it. AI error messages are often calm like a meditation app.

9) Edge cases and product reality - the “missing grit” 🧠🪤

Real systems are untidy. AI outputs often lack that texture.

Examples of “grit” that teams have:

  • Feature flags and partial rollouts (Martin Fowler: Feature Toggles)

  • Backward compatibility hacks

  • Weird third-party timeouts

  • Legacy data that violates your schema

  • Inconsistent casing, encoding, or locale problems

  • Business rules that feel arbitrary because they are arbitrary

AI can handle edge cases if you tell it, but if you don’t explicitly include them, it often produces a “clean world” solution. Clean worlds are lovely. Clean worlds also do not exist.

Slightly strained metaphor incoming: AI code is like a brand-new sponge - it hasn’t absorbed the kitchen disasters yet. There, I said it 🧽. Not my best work, but it’s true-ish.

10) How to make AI-assisted code feel human - and more importantly, be reliable 🛠️✨

If you’re using AI to draft code (and lots of people are), you can make the output dramatically better with a few habits.

A) Inject your constraints up front

Instead of “Write a function that…”, try:

  • expected inputs/outputs

  • performance needs

  • error policy (raise, return result type, log + fail?)

  • naming conventions

  • existing patterns in your repo

B) Ask for trade-offs, not just solutions

Prompt with:

  • “Give two approaches and explain the trade-offs.”

  • “What would you avoid doing here and why?”

  • “Where will this break in production?”

AI is better when you force it to think in risks.

C) Make it delete code

Seriously. Ask:

  • “Remove any unnecessary abstraction.”

  • “Cut this down to the smallest correct version.”

  • “Which parts are speculative?”

AI tends to add. Great engineers tend to subtract.

D) Add tests that reflect reality

Not just:

  • “returns expected output”

But:

If you do nothing else, do this. Tests are the lie detector, and they don’t care who wrote the code 😌.

11) Closing notes + quick recap 🎯

So, what AI Code tends to look like: it often looks clean, generic, slightly over-explained, and a bit too eager to please. The bigger “tell” isn’t formatting or comments - it’s missing context: domain naming, awkward edge cases, and architecture-specific choices that come from living with a system.

Quick recap

  • AI code isn’t one style, but it often trends tidy, verbose, and over-general.

  • The best signal is whether the code reflects your real constraints and product grit.

  • Don’t obsess over detection - obsess over quality: tests, review, clarity, and intent (Google Engineering Practices: Code Review; Software Engineering at Google: Unit Testing).

  • AI is fine as a first draft. It’s not fine as a last draft. That’s the whole game.

And if someone tries to shame you for using AI, frankly… ignore the noise. Just ship solid code. Solid code is the only flex that lasts 💪🙂.

FAQ

How can you tell if code was written by AI?

AI-assisted code often looks a touch too tidy, almost “textbook”: consistent formatting, uniform structure, generic naming (like data, items, result), and even-keeled, polished error messages. It may also arrive with a thicket of docstrings or comments that simply restate obvious logic. The bigger signal isn’t style - it’s the absence of in-the-wild grit: domain language, repo conventions, awkward constraints, and the edge-case glue that makes systems hold.

What are the biggest red flags in AI-generated error handling?

Watch for broad exception catches (except Exception), swallowed failures that quietly return defaults, and vague logging like “An error occurred.” These patterns can hide real bugs and make debugging miserable. Strong error handling is specific, actionable, and carries enough context (IDs, inputs, state) without dumping sensitive data into logs. Over-defensive can be as risky as under-defensive.

Why does AI code often feel over-engineered or over-abstracted?

A common AI tendency is to “look professional” by adding helper functions, layers, and directories that anticipate hypothetical futures. You’ll see generic helpers like process_data() or handle_request() and neat module boundaries that suit a diagram more than your system’s seams. A practical fix is subtraction: trim speculative layers until you have the smallest correct version that matches the requirements you have, not the ones you might inherit later.

What does good AI-assisted code look like in a real repo?

The best AI-assisted code reads as though your team claimed it: it uses your domain terms, matches your data shapes, follows your repository patterns, and aligns with your architecture. It also reflects your risks - beyond happy paths - with meaningful tests and intentional review. The goal isn’t to “hide AI,” it’s to anchor the draft in context so it behaves like production code.

What tests expose “clean world” assumptions the fastest?

Integration tests and edge-case tests tend to reveal problems quickly because AI output often assumes ideal inputs and predictable dependencies. Use domain-focused fixtures and include weird inputs, missing fields, partial failures, timeouts, and concurrency where it matters. If the code only has happy-path unit tests, it can look correct while still failing when someone hits the one untested button in production.

Why do AI-written names feel “technically correct but culturally wrong”?

AI often chooses safe, generic names that work across many projects, but teams develop a specific dialect over time. That’s how you end up with mismatches like userId vs AccountId, or transaction vs LedgerEntry, even when the logic is fine. This naming drift is a clue the code wasn’t written while “living inside” your domain and constraints.

Is it worth trying to detect AI code in code reviews?

It’s usually more productive to review for quality than authorship. Humans can write clean, over-commented code too, and AI can produce excellent drafts when guided. Instead of playing detective, press on design rationale and the points of likely failure in production. Then validate with tests, architecture alignment, and error discipline. Pressure-testing beats vibe-testing.

How do you prompt AI so the code comes out more reliable?

Start by injecting constraints up front: expected inputs/outputs, data shapes, performance needs, error policy, naming conventions, and existing patterns in your repo. Ask for trade-offs, not just solutions - “Where will this break?” and “What would you avoid and why?” Finally, force subtraction: tell it to remove unnecessary abstraction and produce the smallest correct version before you expand anything.

References

  1. Stack Overflow - Stack Overflow Developer Survey 2025 - survey.stackoverflow.co

  2. GitHub - GitHub Octoverse (Oct 28, 2025) - github.blog

  3. Google - Google Engineering Practices: The Standard of Code Review - google.github.io

  4. Abseil - Software Engineering at Google: Unit Testing - abseil.io

  5. Abseil - Software Engineering at Google: Code Review - abseil.io

  6. Abseil - Software Engineering at Google: Larger Testing - abseil.io

  7. Martin Fowler - Martin Fowler: Feature Toggles - martinfowler.com

  8. Martin Fowler - The Practical Test Pyramid - martinfowler.com

  9. OWASP - OWASP Threat Modeling Cheat Sheet - cheatsheetseries.owasp.org

  10. OWASP - OWASP Logging Cheat Sheet - cheatsheetseries.owasp.org

  11. OWASP - OWASP Top 10 2025: Security Logging and Alerting Failures - owasp.org

  12. ESLint - ESLint Docs - eslint.org

  13. GitHub Docs - GitHub CodeQL code scanning - docs.github.com

  14. TypeScript - TypeScript: Static Type Checking - www.typescriptlang.org

  15. mypy - mypy documentation - mypy.readthedocs.io

  16. Python - Python docs: The Python Profilers - docs.python.org

  17. pytest - pytest fixtures docs - docs.pytest.org

  18. Pylint - Pylint docs: bare-except - pylint.pycqa.org

  19. Amazon Web Services - AWS Prescriptive Guidance: Retry with backoff - docs.aws.amazon.com

  20. Amazon Web Services - AWS Builders’ Library: Timeouts, retries and backoff with jitter - aws.amazon.com

Find the Latest AI at the Official AI Assistant Store

About Us

Back to blog