A developer recently discovered that AI-generated documentation is deceptively easy to write but frustratingly hard to understand weeks later. The initial effort feels minimal, but the resulting readmes and Confluence pages lack the clarity needed for future reference. This creates a hidden technical debt for teams who rely on these docs for onboarding and maintenance.

The core problem isn't the AI's speed but its inability to capture nuanced context and intent. Human-written documentation typically includes reasoning and decisions that AI models often miss. For engineering teams, this means critical knowledge gaps emerge, slowing down collaboration and increasing the risk of errors when revisiting projects after a few months.

Moving forward, teams should treat AI-generated text as a first draft, not a final product. The best practice involves a human review cycle to add clarity, examples, and context. This hybrid approach balances efficiency with long-term maintainability, ensuring documentation remains a reliable asset rather than a forgotten liability.