How to Solve AI Tech Debt
How to Solve AI Tech Debt: Video Overview
AI-fueled velocity magnifies the weak seams that already existed in your engineering process. Point human judgment at upstream decisions—where to review, how to codify standards, and how early alignment happens—so speed and quality scale together.
Key takeaways
- Inspect data models, traffic patterns, and future extensibility—because AI-generated code already excels at syntax and tests.
- Keep quality guardrails living with the code via shared rulebooks, AI reviewers, and automation that continuously evolves your standards.
- Shift review into the planning doc so senior intuition shapes the blueprint before commits, keeping speed and quality aligned.
Transcript summary
Know where to look (00:00 – 03:30)
AI doesn't inherently create technical debt; it exposes neglected design decisions faster. Human review should concentrate on schema choices, production behavior, and long-term extensibility instead of syntax nits that AI already handles. By monitoring data modeling rigor, real-world traffic quirks, and forward compatibility, you spot the cracks before they spread.
Tighten feedback loops (03:30 – 05:35)
Legacy loops rely on wiki docs, noisy linters, and ad-hoc Slack threads. Modern teams bring their rulebooks into the repo, apply AI reviewers to reinforce standards on every pull request, and automate the conversion of repeated findings into enforceable rules. The loop spots cracks quickly and teaches the system not to repeat them.
Shift review left (05:35 – 07:58)
Most AI work starts from a plan, so move peer review there. Evaluate corner cases, integration points, API design, ownership boundaries, and tech choices before coding begins. Early review transforms veteran scar tissue into shared judgment, preventing expensive rewrites and keeping AI velocity aimed at the right solution.
FAQ
Does AI actually increase technical debt?
AI exposes existing weaknesses by shipping changes faster. Debt grows when teams keep reviewing syntax instead of data models, production behavior, and forward compatibility.
How can we keep AI code reviews consistent across teams?
Centralize standards in the repository, pair them with AI reviewers that reference those rules on every pull request, and automate the promotion of recurring findings into hardened policies.
What happens when review shifts into the planning doc?
Senior engineers share pattern recognition earlier, so design trade-offs are resolved before implementation. That prevents costly rewrites and keeps AI velocity pointed at the right solution.
Catch AI-Driven Issues Before They Ship
Propel keeps your engineering standards living in the repo, turns fresh review insights into automation, and highlights drift from intent.
