Most Common Python Errors When Using AI

Introduction

Python is a favorite among backend developers, particularly in the fast-paced world of startups where flexibility and speed are key. But let’s be honest—Python’s simplicity can sometimes be deceiving, and even the most seasoned developers can find ourselves pulling our hair out trying to understand why the code doesn’t work.

In this article, we’ll dive into the top 10 Python errors that backend developers frequently encounter, especially in smaller startup teams. We’ll explain why these errors happen and provide tips on how to avoid them. We’ll also look at whether the common AI tools are likely to make the errors or can help you spot and fix them.

Table of Contents

1. Introduction
2. Error 1: TypeErrors
3. Error 2: NameErrors
4. Error 3: IndexErrors
5. Error 4: KeyErrors
6. Error 5: ImportErrors
7. A Recurring Theme in the Python Errors AI Makes
8. How Fine Can Help You Avoid These Errors
9. Conclusion

Error 1: TypeErrors

TypeErrors occur when you try to perform an operation on incompatible data types, like trying to add a string to an integer. It’s the programming equivalent of mixing oil and water—not going to happen. Python is dynamically typed, meaning variables don’t have fixed types, but this flexibility can sometimes lead to unexpected type mismatches.

How to Avoid: Always check the data types you’re working with, and consider using type hints in your function definitions to ensure compatibility. AI models might try to perform operations on incompatible types or fail to include necessary type conversions. Fine will test to make sure there are no TypeErrors and fix any that occur. Fine’s PR review feature also identifies TypeErrors in GitHub PRs that human developers have written, ensuring that they don’t make it into production.

Error 2: NameErrors (Shock horror, they’re back)

No serious Python developer goes around making NameErrors anymore, right? Well, here’s the thing.

ChatGPT and other AI coding tools make NameErrors all the time. They can’t help it. They don’t know what you’ve named things in your code, so they have to make a logical guess or use a placeholder - which will usually be wrong. Even the best models, such as o1 and Claude Sonnet 3.5, are helpless here. In fact, many people accuse AI of “hallucinating”, when really they’re referring to a simple NameError, where the AI isn’t to blame.

Whilst simple to fix (in theory), this is quite a pain and every developer should have their eyes open for NameErrors (and indentation issues also - remember those?) when using AI coding tools that don’t have full context awareness.

Fine is the AI coding tool to choose to avoid these errors. Because it indexes your repositories and issues, the AI can identify the correct names in your codebase and save you hours of debugging.

Error 3: IndexErrors

IndexErrors arise when you try to access an index that doesn’t exist in a list. Imagine trying to grab the fifth apple from a basket that only has four—you’re bound to run into trouble. This often happens due to off-by-one errors, where the index is either too high or too low. If you’re not familiar with how python’s range works or how slicing works, it’s a good idea to sharpen up on it to avoid index errors.

How to Avoid: Double-check your list boundaries and validate your input data. Fine can highlight potential IndexErrors, helping you avoid those pesky off-by-one mistakes. This is particularly useful in scenarios where your code dynamically generates or manipulates lists, which can lead to unpredictable indexing issues​.

Error 4: KeyErrors

KeyErrors happen when you try to access a dictionary key that isn’t present. It’s like asking for the keys to a car that you don’t own—not going to get far!

How to Avoid: Use the .get() method or check for key existence before access. Fine can help by suggesting safe dictionary access patterns, reducing the risk of a KeyError.

Error 5: ImportErrors

ImportErrors happen when a module isn’t imported correctly, either because it’s missing, you’ve made a typo in the import path, or you’ve created a circular import. These errors are common when managing dependencies across different environments. Imagine - File A tries to import File B, which tries to import File A.

How to Avoid: Ensure your modules are properly installed and avoid overly complex import chains. Fine’s AI can track your imports and warn you about potential issues, making it easier to manage your dependencies.

A Recurring Theme in the Python Errors AI Makes

NameErrors, ImportErrors, AttributeErrors, KeyErrors - AI will keep making these mistakes as long as it doesn’t have the full context of your codebase. It’s like if I were to ask you to write code for my platform, but without showing you my existing repo. How would you know what to refer to?

How Fine Can Help You Avoid These Errors

Fine is designed to be your coding companion, catching these common Python errors before they can trip you up. Using advanced AI algorithms, Fine provides real-time feedback, highlights potential issues, and offers tailored suggestions to keep your code clean and error-free.

Whether you’re dealing with indentation issues, NameErrors, or TypeErrors, Fine acts as your second pair of eyes, ensuring that your development process remains smooth and efficient. With Fine integrated into your workflow, you can focus on what really matters—building great software.

Conclusion

Python errors can be a major headache, especially in a startup environment where every line of code counts. By understanding these common errors and how to avoid them, you can write cleaner, more reliable code. And with Fine by your side, you’ll catch and fix these errors effortlessly, keeping your projects on track and your sanity intact. Be careful of code generators that aren’t aware of your existing codebase, such as ChatGPT and GitHub Copilot—they’re more likely to make simple Python mistakes.

Ready to take your Python development to the next level? Try Fine today with our free trial and see how our AI-powered coding assistant can help you write error-free code faster. Sign up now, or schedule a demo to discover how Fine can integrate seamlessly into your workflow and boost your team's productivity. Don’t let simple errors slow you down—let Fine handle the details so you can focus on building great software.