When AI Breaks Your App: The Hidden Struggle of Fixing Generated Code

TL;DR

AI-generated code is notorious for looking correct but behaving unpredictably. It often contains hidden complexity, implicit assumptions, missing edge cases, and silent failures wrapped inside overly confident abstractions. These patterns create a form of Synthetic Technical Debt (STD), clean-looking code that conceals structural risk.

This blog outlines the Untrusted Code Recovery Model (UCRM), a structured approach for repairing AI-generated codebases by treating them like hostile third-party libraries until they are stabilized, refactored, and re-verified.

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The Problem: The Illusion of “It Looks Fine”

Anyone inheriting AI-generated code knows the feeling: The file opens… it appears clean… but something feels wrong.

Unlike human-written code, which often exposes intent through naming, structure, and comments, AI-generated code is performative, it imitates good engineering patterns without understanding why they matter.

This creates consistent, cross-language failure modes:

Hidden Complexity

AI frequently solves simple problems with unnecessarily abstract or nested code, sometimes adding entire libraries for trivial operations. This makes the code hard to reason about and harder to safely modify.

Incorrect Assumptions

LLMs default to idealized environments:

• Perfect networking
• Expected input formats
• Standard file paths
• Always-existing environment variables

These assumptions collapse instantly in real production systems.

Missing Edge Cases

AI-generated code focuses aggressively on the happy path. Unexpected user input, API timeouts, race conditions, and null states often go entirely unhandled.

Silent Failures

The worst offender: try { … } catch (e) { console.error(e); }

This leads to corrupted state, misleading logs, and data inconsistencies that only appear days later in customer reports.

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Core Concept: Synthetic Technical Debt (STD) and the Untrusted Code Recovery Model (UCRM)

The systemic fragility of AI-generated code stems from Synthetic Technical Debt (STD), code that appears modern, structured, and "best practice-ish" but is missing the invisible layers that make software safe under load, failure, or ambiguity.

To repair STD, we use the Untrusted Code Recovery Model (UCRM).

UCRM: Treat all AI-generated code as untrusted until each unit’s intent, boundaries, and error behaviors are validated, refactored, and reinforced with human-grade reliability patterns.

Where the prior post introduced the RCP for backend functions, UCRM is its front-end counterpart: a blueprint for safely adopting AI-generated code.

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Step-by-Step Implementation: Applying UCRM

1. Normalize & De-obfuscate the Structure

AI often produces:

• Functions over 500+ lines
• Nesting 4–8 levels deep
• Repeated logic blocks
• Multiple responsibilities mashed together

Break everything down by hand:

• Split large functions
• Reduce nested flows with early returns
• Promote repeated logic into utilities
• Extract domain operations into modules

This restores readability and developer trust.

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2. Identify Assumptions & Remove Implicit Behaviors

Scan specifically for:

• Hard-coded file paths
• Hidden stateful variables
• Default configuration values
• “Magic” environment assumptions

Replace assumptions with explicit, documented defaults or injected configuration.

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3. Strengthen Boundary & Input Validation

Anywhere data enters or leaves the system:

• Validate it
• Sanitize it
• Guard it

AI rarely protects boundary surfaces properly, which is where most failures originate.

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4. Replace Silent Failures With Predictable Paths

Silent catches, suppressed errors, and vague logs must be eliminated.

Typical AI Error Anti-pattern

try {
  riskyOperation();
} catch (e) {
  console.error("Error:", e);
}

UCRM-Compliant Behavior

• Explicit error classification
• Human-readable, structured logs
• Predictable return value or rethrow
• No swallowed control flow

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5. Re-test Reality, Not the Happy Path

LLMs often generate functions that only work with ideal inputs. Under UCRM:

• Add regression tests for every known bug
• Add integration tests for all external boundaries
• Add chaos tests for error states
• Add concurrency/load tests where relevant

If the testing suddenly surfaces dozens of failures, good, UCRM is doing its job.

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Key Considerations & Trade-offs

The trade-off: UCRM requires human intervention, but avoids delayed explosions in production.

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Don’t Let AI’s Confidence Trick You

AI-generated code has one fatal flaw: It is too confident for its level of understanding.

Humans write code to be maintained. LLMs write code to complete a prompt.

Treat AI code as untrusted, repair it with UCRM, and your systems will survive both the next feature push and the next AI-assisted refactor.

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Worried your AI-generated code is quietly breaking everything? Get an App Unstuck code audit. →