AI technical debt is accumulating rapidly as teams rush to ship AI products without proper planning, testing, or governance. Four major categories are identified: data debt (bias, drift, poisoning, lack of anonymization), model debt (no versioning, no rollback, missing evals, no penetration testing), prompt debt (undocumented system prompts, no input validation, prompt injection vulnerabilities, missing guardrails), and organizational debt (unclear ownership, no governance policy, scalability gaps). Strategic technical debt taken consciously with a remediation plan is acceptable, but reckless debt from poor discipline compounds faster in AI than in traditional software due to AI's probabilistic, non-deterministic nature. The recommended approach is to follow a disciplined project lifecycle: requirements, architecture, implementation, testing, deployment, and evaluation — not skipping steps just because it's AI.
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