Comprehension Debt: What LLM-Generated Code Means for Testing, Design, and Maintenance

Comprehension debt is a ticking time bomb in LLM-generated code. This isn’t just about the code itself—it’s about downstream testing, design reviews, and long-term maintenance. A new engineer at Google can’t hit the ground running without months of comprehension debt. The problem scales as teams rely more on generators and fewer on first-principles explanations ^[1].

Comprehension debt means you end up with code that sounds right, but you can’t readily explain why it works or how it should be changed. The risk isn’t only misinterpretation; it’s the whole chain: testing gaps, brittle design decisions, and maintenance surprises when the original context drifts away. Rely on Git history and issue trackers to reconstruct decisions and references ^[1].

Mitigations in practice: - Generate test cases to drive the code, ensuring edge-case handling is exercised ^[1] - Enforce coding standards on the LLM to generate small, easily comprehensible software modules ^[1] - Use Git history and issue trackers to reconstruct decisions and references when memory fades ^[1] - Document high-level design via architectural decision records (ADRs) or design pages so future engineers understand intent ^[1]

Bottom line: don’t lean on plausibility alone—tie LLM output to tests, design docs, and traceable history. In 2025, the best teams will treat comprehension debt as an active constraint, not a footnote ^[1].

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