When AI Gets Coding Wrong: My Journey With LLM Programming Mistakes

Hey there, fellow coders! After spending countless late nights debugging AI-generated code, I thought I'd share what I've learned about the quirky ways these models mess up. Trust me, recognizing these patterns has saved me hours of head-scratching!

Those Libraries That Don't Exist

Picture this: You're racing to meet a deadline, you ask an AI to help with some visualization code, and you get back something like:

import dataview.charts as dvc

my_chart = dvc.interactive_plot(data, 
                               hover=True,
                               animations="slide-in")
my_chart.save("quarterly_results.html")

Looks perfect, right? Except dataview.charts isn't real! I've been burned by this so many times I now automatically check every import statement before moving forward.

What's happening is that AI models have seen millions of import statements and can create incredibly believable mashups of real libraries. They'll combine features from matplotlib, Plotly, and seaborn into fictional packages that sound totally legitimate.

My rule of thumb: 

If I haven't personally used the library before, I verify it exists before wasting time with the rest of the code.

When Python 2 Meets Python 3 (But Shouldn't)

Last month I was debugging a script that kept failing, and it took me ages to spot the problem:

values = get_user_data()
print "Processing %d records" % len(values)  # Old Python 2 style!
results = {k: v for k, v in process_items(values)}  # Dictionary comprehension (Python 3)

The AI had casually mixed Python 2 print statements with Python 3 dictionary comprehensions! These version mix-ups are super common, especially with:

• Print statements (parentheses or not?)
• Exception handling (as vs comma)
• Division operator behavior
• String formatting methods

What makes this tricky is that the code looks reasonable at a glance. By utilizing Evidently AI to catch these inconsistencies automatically - it's been a game-changer!

The Mysterious Changing Variable Names

This one drives me bonkers. The AI starts with one naming scheme, then halfway through the function it's using completely different variable names:

def calculate_monthly_metrics(sales_data):
    # Extract monthly figures
    monthly_totals = group_by_month(sales_data)
    
    # Calculate growth percentages
    growth_rates = []
    for i in range(1, len(monthly_figures)):  # Wait, what happened to monthly_totals??
        growth = (monthly_figures[i] - monthly_figures[i-1]) / monthly_figures[i-1]
        growth_rates.append(growth)

Did you catch it? The AI switched from monthly_totals to monthly_figures mid-function. 

I've started doing quick searches for each variable name when reviewing AI code to catch these issues early.

Logic That Would Make Escher Proud

Sometimes AI-generated code contains logical impossibilities that are harder to spot. Check out this beauty:

def process_payment(amount, user_credit):
    if amount <= user_credit:
        # User has enough credit
        remaining_credit = user_credit - amount
        return {"success": True, "remaining": remaining_credit}
    elif amount > user_credit:
        # Not enough credit
        return {"success": False, "shortage": amount - user_credit}
    else:
        # This can literally never happen!
        logger.warning("Unexpected payment condition")
        return {"success": False, "error": "unknown_condition"}

The final else clause can NEVER execute because we've already covered all possible relationships between amount and user_credit. Yet the AI confidently includes error handling for an impossible scenario!

Started using Arize AI's code analysis tools recently, which catches most of these logical dead-ends before they make it into production.

Config Parameters From Parallel Universes

When working with libraries that have complex configuration options, AI models often invent parameters that look plausible but don't actually exist:

# Connecting to a database
connection = mysql.connector.connect(
    host="db.example.com",
    user=os.environ.get("DB_USER"),
    password=os.environ.get("DB_PASSWORD"),
    database="customer_records",
    connection_timeout=30,  # Real parameter
    retry_backoff=2.5  # Completely made up!
)

The retry_backoff parameter looks totally reasonable (and maybe it should exist!), but it's not actually a valid option for MySQL connector. The AI has probably seen similar parameters in other database libraries and mixed them together.

My Real-World Survival Guide

After countless hours fixing these issues, here's what actually works for me:

1. Read line by line, not just the overall logic. Many hallucinations only become obvious when you slow down and consider each line individually.

2. Check documentation for any unfamiliar functions or parameters. I keep multiple browser tabs open just for this.

3. Run small chunks first. I never try to run a large block of AI-generated code all at once anymore. Test in small pieces!

4. Use a consistent style guide and ask the AI to follow it. This reduces the chances of getting mixed Python versions or inconsistent naming.

5. Tell the AI about your environment specifically. I've found saying "I'm using Python 3.10 with pandas 2.0.3" dramatically reduces version-related errors.

I've been experimenting with automated tools from companies like Arize and Evidently to catch these issues. While they're not perfect, they've helped our team reduce debugging time by almost 40% on AI-assisted projects.

The Future Is Still Bright

Despite these quirks, I'm still amazed by how much these tools have accelerated my workflow. The key has been learning to spot their weaknesses and compensate accordingly.

I'm convinced that understanding these patterns is becoming an essential programming skill. The developers who can effectively partner with AI—understanding both its strengths and limitations—are going to have a serious advantage.

What weird AI coding mistakes have you encountered? Drop your experiences in the comments! I'm always looking to expand my "watch out for this" checklist.

Until next time, happy (and hallucination-free) coding!