AI technical debt is harder to see than ordinary code debt. It hides in prompts nobody owns, evaluations nobody runs, stale embeddings, unpinned model behavior, and workflows where every small change feels risky.
This hub connects the older technical debt writing with the newer AI-specific debt pattern: invisible system behavior that keeps returning 200 OK while quality quietly degrades.
Prompts become legacy code when they are copied from demos, edited without review, and never tied to expected behavior.
Teams without evals argue about quality using anecdotes. That makes every model, prompt, or retrieval change risky.
Stale embeddings, missing documents, drifting labels, and weak source tracking create confident wrong answers.
Provider-specific logic and tool calls buried in application code make upgrades feel like surgery.
Start with the highest-leverage stabilizers:
AI debt doesn't look like normal tech debt. It hides in prompts nobody owns, evals nobody runs, and data pipelines nobody watches. By the time you notice, every change feels dangerous.
AI features create a new species of technical debt that hides in prompts, data pipelines, and model versions. By the time you notice it, the cleanup bill is brutal.
The term 'technical debt' has become meaningless. Everything inconvenient is debt. Here's what it actually is, when it matters, and why most teams handle it wrong.
A framework for cataloging technical debt, scoring it by impact and risk, and scheduling paydown without stalling feature delivery.
A two-number scoring system for tech debt that tells you what to fix now, what to schedule, and what to quietly accept.
A pragmatic look at technical debt in 2016: what it is, how it shows up, how to measure it, and how to make a business case for paying it down without stalling delivery.