Twelve Principles of AI‑Native Engineering
Rules first. Planned emergence of code.
Context. Working hands‑on with frontier models (e.g., GPT‑5 Pro) flipped a switch for me: refactoring costs collapsed, and the real bottleneck became clarity of rules, not keystrokes. These are field notes from that shift.
1) Plan the Rules; Let Code Emerge
Define behavior, boundaries, and invariants up front. Implementation is a momentary expression of those rules.
2) Documentation Is the Primary Artifact
Specs, examples, ADRs, and constraints are the model’s working memory. Keep them living, exact, and close to the code.
3) Schemas Are Sacred
Types and validation at every boundary are the ground truth. When schemas are solid, everything else can change safely.
4) Tests Bind Intent to Reality
Treat tests as executable intent. They anchor regeneration and prevent drift when the model rewrites large surfaces.
5) Prefer Regenerability Over Cleverness
Optimize for code you can rewrite in hours. Choose clarity and seam design over novelty and hidden magic.
6) Stability Lives in Interfaces
Freeze contracts, not functions. Internal implementations are free to evolve as long as the edges hold.
7) Curate Context Like a Workspace
AI learns in‑context. Groom prompts, examples, and decision logs with the same care you give modules and APIs.
8) Compose Small, Swappable Parts
Design clean seams so components can be hot‑swapped with minimal shock. Modularity turns churn into iteration.
9) Make Change Cheap—and Safe
Automate checks, observe everything, and favor idempotent operations. The faster you can safely change, the faster you learn.
10) Build Systems That Build Systems
Invest in generators, scaffolds, and templates. Meta‑tools compound; micro‑tweaks depreciate.
11) Humans Govern; AI Assembles
Our highest leverage is crisp intent, constraints, and review. Let models do the assembly; we own the rules.
12) Principles Outlive Implementations
Design for durability at the rule layer and disposability at the code layer. That’s how velocity compounds without chaos.
This isn’t vibe coding. It’s planned emergence:
write the rules → generate the code → verify with tests → regenerate without fear.
If this resonates, I’d love to hear how these principles map to your practice.
Concept framed by me, draft generated with AI.