AI doesn't write perfect Python; it writes statistically probable Python. If you're tired of "hallucinated" libraries and broken dependencies, it's time to change how you use LLMs in your workflow.

I’ve spent the last decade breaking and fixing Python environments. Lately, the "breaking" part usually happens because a developer trusted a prompt more than their own eyes. We've reached a weird point in 2026. Everyone’s using AI to generate code, but half the people are just generating faster technical debt.

The honeymoon phase of "Hey, write me a script to scrape this site" is over. We're now dealing with the fallout of AI-generated spaghetti code that looks clean but fails under the slightest load.

The Context Window Lie

Here’s the thing: most devs think their AI "understands" their codebase. It doesn't. Even with the massive context windows we have now, the AI is still just guessing the next token.

I saw a team lose an entire weekend because an LLM suggested a non-existent async method for a niche database driver. It looked perfectly valid. The syntax was gorgeous. But it didn't exist. They spent six hours debugging a "hallucination" because they didn't bother to check the actual docs or run a basic type check.

What’s Actually Working Right Now

If you want to ship code that doesn't trigger a 2 a.m. PagerDuty alert, you've got to stop using AI as a writer and start using it as a collaborator.

1. Agentic Coding Loops

Don't just ask for a function. Use tools that run the code, see the error, and fix it. Modern setups using Cursor or Windsurf are miles ahead of a web-chat interface. They have "agentic" capabilities they can actually see your file structure and run pytest in the background.

2. Type Hinting is Your Best Friend

I used to be a bit loose with Python’s dynamic nature. Not anymore. In the AI era, Pydantic and strict type hinting are non-negotiable. If you don't define your data structures, the AI will invent them. Forcing the AI to adhere to a BaseModel saves you from a world of runtime errors.

3. The "Test-First" Prompt

I've found that the only way to keep the AI on the rails is to give it the test case first. Tell it: "Here is a Pytest file. Write the logic in services.py that makes these tests pass." It flips the script. Instead of you checking the AI's math, the AI is checking its own logic against your requirements.

AI Tool Comparison for Python Devs (2026)

A Real-World Disaster (And the Fix)

Last month, I was consulting for a fintech startup. They’d used an AI to generate a migration script from an old SQL monolith to a distributed FastAPI setup. The AI used a deprecated SQLAlchemy pattern that didn't handle connection pooling correctly under high concurrency.

The site stayed up for ten minutes after launch. Then, as soon as the traffic hit, the DB connections spiked and the whole thing crawled to a halt. We didn't fix it with more AI prompts. We fixed it by rolling back, writing a manual connection manager, and then using the AI to audit that manager for edge cases.

Lesson learned: Use the AI to find flaws in your logic, not to provide the logic from scratch.

The Final Verdict

AI is a power tool, not a replacement for a brain. If you're building in Python right now, don't just ask for code. Ask for "Why?" and then verify the "How."

• Don't trust a library you haven't heard of.
• Do use mypy and ruff on everything the AI spits out.
• Do keep your functions small. AI is much better at writing a 10-line utility than a 200-line class.

The goal isn't to write less code. It's to write better code, faster. Stay skeptical.