The Invisible Debugging Guide: Finding What Your LLM Didn't Tell You
Ever had that frustrating moment when code generated by an AI runs perfectly in testing but crashes spectacularly in production? You're not alone. After several of these experiences, I've learned to spot what's missing from AI-generated solutions before they cause real problems.
The Dangerous World of Missing Error Handlers
Recently, I deployed what seemed like perfectly functional code generated by my favorite LLM. The testing phase went smoothly, but later late at night my phone started buzzing with alerts. What happened?
The code handled the happy path beautifully but contained zero error handling for network timeouts. When our third-party payment processor experienced hiccups, the entire checkout flow crashed rather than gracefully degrading.
# What the LLM gave me
def process_payment(payment_info):
response = payment_gateway.charge(
amount=payment_info.amount,
card=payment_info.card_token,
currency=payment_info.currency
)
return {
"success": True,
"transaction_id": response.transaction_id,
"timestamp": datetime.now()
}
No timeout handling. No network error catching. No validation for the response structure. In testing with our reliable staging environment, these issues never surfaced.
Here's what I should have asked for:
# What I needed
def process_payment(payment_info):
try:
response = payment_gateway.charge(
amount=payment_info.amount,
card=payment_info.card_token,
currency=payment_info.currency,
timeout=5.0 # Explicit timeout
)
# Validate response has expected fields
if not hasattr(response, 'transaction_id'):
logger.error("Invalid payment response structure")
return {"success": False, "error": "invalid_gateway_response"}
return {
"success": True,
"transaction_id": response.transaction_id,
"timestamp": datetime.now()
}
except ConnectionError:
logger.warning(f"Payment gateway connection error for amount {payment_info.amount}")
return {"success": False, "error": "gateway_connection", "retry_after": 30}
except Timeout:
logger.warning(f"Payment gateway timeout for amount {payment_info.amount}")
return {"success": False, "error": "gateway_timeout", "retry_after": 15}
except Exception as e:
logger.error(f"Unexpected payment error: {str(e)}")
return {"success": False, "error": "unknown", "message": str(e)}
LLMs consistently skip error handling unless explicitly asked. They focus on the expected behavior and rarely address failure modes without prompting.
The Missing Edge Cases Pattern
Through painful experience, I've identified specific categories of edge cases that LLMs routinely overlook:
1. Empty Collections
AI models rarely handle empty lists, dictionaries, or sets properly. When I asked for code to calculate average order value, the LLM gave me:
def calculate_average_order(orders):
total = sum(order.amount for order in orders)
return total / len(orders) # Boom! Division by zero if orders is empty
2. Resource Cleanup
Many AI-generated code snippets neglect to release resources, particularly in error scenarios:
def process_large_file(filename):
file = open(filename, 'rb')
data = file.read()
results = analyze_data(data)
file.close() # Never reached if analyze_data raises an exception
return results
The fix is simple (use context managers), but consistently missed.
3. Boundary Values
LLMs rarely address integer overflow, string length limitations, or other boundary conditions:
// Calculating time difference in milliseconds
const timeDiff = endDate.getTime() - startDate.getTime();
const daysDifference = timeDiff / (1000 * 60 * 60 * 24);
What happens when crossing daylight saving time boundaries? Or when dates are in different timezones? The model didn't consider these cases.
My Three-Step Gap Detection Process
After months of patching holes in AI-generated code, I've developed a system for quickly identifying what's missing:
Step 1: Ask "What If It Fails?"
For each external interaction (API calls, file operations, database queries), I explicitly ask:
- What happens if the connection fails?
- What if the operation times out?
- What if the returned data isn't in the expected format?
This simple question uncovers 80% of missing error handling.
Step 2: Feed It Empty or Extreme Inputs
I mentally trace code execution with:
- Empty collections ([], {}, "")
- Extremely large values
- Negative numbers (when only positive are expected)
- Unicode characters in string inputs
When reviewing an LLM-generated function that processed user comments, I noticed it would crash with emoji inputs - something not mentioned in the otherwise detailed code comments.
Step 3: Check Resource Management
For any code that acquires resources (files, network connections, database handles), verify it properly releases them in all scenarios, including exceptions.
A colleague of mine found that an LLM-generated script that processed images would leave hundreds of temporary files behind when run in production, eventually filling disk space.
Real-World Example: The Project That Almost Failed
Using AI assistance saved tremendous time, but nearly cost us the project until we applied this gap-detection process.
The LLM created elegant code for transferring customer records between database systems. It looked comprehensive and even included progress tracking. But when we ran our gap analysis, we discovered critical missing pieces:
- No validation that destination records matched source structure
- No handling for dropped connections during long-running transfers
- No mechanism to resume partially completed transfers
- No verification step to compare source and destination records
After addressing these gaps, we ran a pilot migration that encountered three of these exact issues! Had we deployed the original code, we would have ended up with corrupted or incomplete customer data.
Prompting Techniques That Force Completeness
I've found that changing how I prompt LLMs dramatically reduces these gaps:
-
Explicitly request error handling: "Include comprehensive error handling for all external operations"
-
Specify the environment: "This will run in a production environment with unreliable network connectivity"
-
Ask for test cases: "Include sample test cases that would verify edge case handling"
-
Request comments about limitations: "Add comments about any assumptions or limitations in this implementation"
Using these prompting techniques reduced our bug rate from AI-generated code by approximately 70%.
The Future of Gap-Free AI Coding
As models continue to improve, I expect some of these issues to diminish, but the fundamental challenge remains: LLMs optimize for the happy path because that's what most code examples show.
The most successful developers who use AI coding tools have all developed their own version of gap analysis. They view the AI as generating a first draft that needs human oversight focused specifically on what's missing rather than what's there.
By systematically looking for these gaps, you'll save yourself countless debugging hours and dramatically improve the reliability of AI-assisted code.
What gaps have you found in AI-generated code?
I'd love to hear about your experiences in the comments below!
