AI-Powered begginer friendly python error messages

Ideas
1

Dear Python Developers,

I would like to propose an AI-powered error handling system** that can make Python’s debugging experience more beginner-friendly and context-aware.

Problem Statement:

Currently, Python’s error messages tell what went wrong, but they don’t always provide the best possible fix. Beginners often struggle to understand the issue and how to fix it.

Proposed Solution:

  1. Context-Aware Error Detection
    Instead of just showing an error, Python should
    analyze the mistake and suggest a possible fix
    Example:

a = input("Enter a number: ")
print(a + 5)

Current Error: TypeError: can only concatenate str (not “int”) to str Smart Suggestion: "Did you mean to convertato an integer? Try usingint(a)`."
2. AI-Powered Error Suggestions
If Python detects an error, it should provide multiple possible fixes based on the context.
Example:

Possible Fixes:

  1. Convert a to integer → print(int(a) + 5)
  2. Convert 5 to string → print(a + str(5))

Users can select the best fix interactively.
3. Self-Learning Debugging Assistant
Using Machine Learning, Python can **learn from common mistakes and provide better suggestions over time.

Implementation Plan:

To test this idea, I have developed a basic Python script that detects errors and suggests possible fixes. This can be extended into an AI-powered debugging system for Python.

Would love to hear your thoughts!

Best regards,
Ahmed Raza

Basic Python Script for Smart Error Handling

def smart_debugger(code):
try:
exec(code)
except TypeError as e:
if “can only concatenate str” in str(e):
print(“:cross_mark: Error:”, e)
print(“:white_check_mark: Possible Fix: Convert number to string using str() or input to integer using int().”)
else:
print(“:cross_mark: Error:”, e)
except Exception as e:
print(“:cross_mark: Error:”, e)

Example usage:
user_code = “”"
a = input("Enter a number: “)
print(a + 5)
“””
smart_debugger(user_code)

2

Using an AI requires either local resources to run a large LLM model, or a network connection to a hosted service that is willing to run it for you. Both rule it out as a feature that could be in Python itself.

However, you can write it. Python offers sys.excepthook which lets you customise how uncaught exceptions are displayed. You can use this to catch uncaught exceptions, use the traceback module to format them, and pass them to an LLM for summary.

7 Likes
3

Some thing to keep in mind: Large Language Models (LLM) are known to hallucinate i.e. invent things that simply do not exist.

There is already a third-party module which I wrote (https://friendly-traceback.github.io/docs/index.html) which provide help for hundred of cases, without using LLMs. May I suggest you have a look at it and see if it satisfies your needs?

(N.B. friendly/friendly-traceback has not been updated in a couple of years as I had to take a break from programming. However, it still works.)

9 Likes
4

I was going to suggest read a recently related thread (this one).

I think implementing this solution in not feasible in many ways. Maybe we could discuss friendlier error messages or more context aware messages…

My conclusion in a recent thread about error handling was that error messages are for humans and shouldn’t be used as part of the logic of the code -which is common sense -.

Edit: I confused the last message with the OP

5

There seems to be a hidden assumption that the rest of the code is fine and its analysis can help to correct the one offending line. I wouldn’t rely on that. Especially not with beginners’ code.

4 Likes