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Manoj Mishra
Manoj Mishra

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The Validation Gap – Why You Can’t Trust AI Blindly

#ai #codequality #codereview #testing

Introduction

AI can generate code faster than any human, but it doesn’t understand your business logic, your data, or your quality standards. In this post, we cover five critical validation mistakes that lead to undetected bugs, technical debt, and production failures.

Mistake 1: Trusting AI‑Generated Code Without Review

Description: Directly copying AI‑generated code into production without manual review or testing.

Realistic Scenario: Developer uses AI to generate encryption code that uses insecure ECB mode but doesn’t review it.

❌ Wrong Prompt:

Write AES encryption for sensitive data in Java
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Developer copies code without reviewing cryptography choices.

⚠️ Why it is wrong: AI may generate code with insecure defaults (ECB mode, static IV, weak key derivation).

✅ Better Prompt:

Write AES-GCM encryption for sensitive data in Java.

I will review the code. Please include:

Proper IV generation (random, non-repeating)

Key derivation using PBKDF2

Authentication tag verification

Comments explaining each step for security review

After review, I'll validate with test vectors.
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💡 What changed: Acknowledged need for review and requested explicit security features.

Mistake 2: Not Running Tests After AI Changes

Description: Applying AI‑suggested refactoring without running test suites to catch regressions.

Realistic Scenario: AI suggests optimizing a service method. Developer applies changes and deploys without running tests.

❌ Wrong Prompt:

Optimize this service method to reduce database calls
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Developer applies code and deploys.

⚠️ Why it is wrong: Optimization may break edge cases, transaction boundaries, or business logic not captured in initial prompt.

✅ Better Prompt:

Optimize this service method to reduce database calls.
Current test suite covers:
Happy path
Null inputs
Concurrent access
After generating optimized version, I will:
Run full test suite (unit + integration)
Verify test coverage doesn't drop
Performance test locally before PR
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💡 What changed: Added validation steps to catch regressions.

Mistake 3: Assuming AI Understands Business Logic

Description: Relying on AI to correctly implement complex business rules without providing domain context.

Realistic Scenario: AI generates discount calculation code but doesn’t know that discounts cannot stack with certain promo codes.

❌ Wrong Prompt:

Write discount calculation function
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⚠️ Why it is wrong: AI doesn’t know business rules like “new users get 20% but can’t combine with referral discount.”

✅ Better Prompt:

Implement discount calculation with these business rules:

New users: 20% off first purchase, cannot combine with any other discount

Referral discount: $10 off, can combine with seasonal sales

Seasonal sale: 15% off, applies to all items except clearance

Clearance items: no additional discounts

Maximum discount: 50% of original price

Function signature: calculateDiscount(User user, List<Item> items, String promoCode)
Return final price and breakdown of applied discounts.

Edge cases: promo code expired, user not eligible, items mix of clearance/non-clearance.
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💡 What changed: Explicit business rules prevent logic errors.

Mistake 4: No Edge Case Validation

Description: AI generates code that works for happy path but fails on nulls, empty lists, or boundary values.

Realistic Scenario: AI generates list processing code that throws NullPointerException when input list is null.

❌ Wrong Prompt:

Write function to process user list and calculate average age
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⚠️ Why it is wrong: Generated code assumes non-null, non-empty list.

✅ Better Prompt:

Write function to calculate average age from user list.

Handle edge cases:

Null input: return Optional.empty()

Empty list: return Optional.empty()

Users with null age: skip them (don't include in count)

Large list (>1M): avoid OOM, use streaming

Provide unit tests for all edge cases.
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💡 What changed: Explicit edge case handling prevents production crashes.

Mistake 5: Skipping Static Analysis After AI Modifications

Description: Not running linters, formatters, or static analysis on AI‑generated code.

Realistic Scenario: AI generates Python code that passes pylint checks but uses deprecated libraries.

❌ Wrong Prompt:

Write Python script to parse JSON logs
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Developer runs script manually, bypasses CI checks.

⚠️ Why it is wrong: Code may have style violations, unused imports, or security issues not caught by manual run.

✅ Better Prompt:

Write Python 3.11 script to parse JSON logs.

After generation, I will:

Run pylint with project config (.pylintrc)

Run mypy for type checking

Run bandit for security scanning

Format with black

Please follow PEP 8 and include type hints.
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💡 What changed: Added validation steps to maintain code quality standards.

Summary & Best Practices

  • Treat AI‑generated code as a draft—always review before commit.
  • Run the full test suite after any AI‑suggested change.
  • Provide business rules explicitly in your prompts.
  • Include edge cases in both prompts and testing.
  • Use static analysis tools as part of your AI workflow (linters, type checkers, security scanners).

Validation turns AI‑generated code from a liability into a reliable building block.

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