AI for Python Code: What Works Better

Python is the language where AI tools perform best, and simultaneously the language where their mistakes are hardest to spot. Here is both sides.

What makes Python tasks distinctive

Abundant training data. Python is widely represented and typical tasks are uniform, so the model confidently produces working code for scripts, parsers, glue and data processing.

Dynamic typing. The flip side: the model lacks the structural hint a compiler provides in typed languages. It does not know what will arrive in a function, and the error surfaces at runtime on real data rather than while writing.

Fast-moving libraries. Training data contains more old versions than new. Hence the most common problem: a confidently written call that was current three years ago.

No compile step. Code that looks correct runs and fails halfway. In a typed language some of those errors are caught before execution.

What it handles well

• Scripts and glue. One-off tasks, file processing, format conversion.
• Parsing and data work, especially where the structure is known.
• Tests for existing code. Comes out well and is easy to verify.
• Explaining unfamiliar code. Describing what a module does is a strength.
• Routine refactoring. Renames, extracting functions, uniform edits.

Where it errs systematically

Four mechanisms worth recognising:

Outdated library calls. Code written against a previous major version. It looks right and fails at runtime or, worse, behaves differently. The fix is putting the version in context: the dependency file should be visible.

Broad exception handling. A construct that catches everything and swallows the error is a common pattern in generated code. The result: the error leaves the logs and the problem stays.

Relying on ordering that is not guaranteed. The code works on test data and breaks on real data because it assumes an order nothing promises.

Unclosed resources. Files, connections, sessions. Invisible in a short task, cumulative in a long-running process.

What all four share: they surface on real data rather than at writing time, which is why a skim does not catch them.

Techniques that work

In descending order of effect:

1. Type annotations. They give the model structure a dynamic language otherwise lacks and let static checking catch some errors before runtime. Generation quality on a typed project is noticeably higher.
2. A test as the task statement. “Write a function that makes this test pass” is a checkable task the tool can close itself by running it.
3. The dependency file in context. The model sees real versions and stops writing against an old API.
4. A virtual environment and isolation, especially if the agent may install packages - see sandboxing.
5. A linter and formatter in the project. Some problems are fixed automatically and never need your attention.
6. Explicit project conventions. Where modules live, how errors are handled, what is forbidden. Worth writing down once - see skills.

A note on dependencies

A Python-specific caution: the model may suggest a package that does not exist or one named similarly to a real one. This is not hypothetical: names close to popular packages are a known distribution route for malicious code.

The rule is simple: a new package in the dependency list is a reason to look it up. Especially when it appeared in a diff you skimmed.

Choosing a tool and model: AI for writing code and choosing by task. The underlying mechanics: how it works. The overview is in the vibe coding guide.