In my latest blog post, I break down how to prevent these expensive pitfalls by creating AI Skills Documentation. If you want to stop treating AI like a generic syntax generator and start treating it as a context-aware partner, here are the 4 steps you need to take:

1️⃣ Establish a Standards Directory: Build your "enterprise memory." Document your boundaries, naming conventions, and PII handling so AI doesn't fall back on destructive anti-patterns.

2️⃣ Develop Governance Templates: Don't just ask for Python code. Force AI to generate governed solutions—accompanying YAML files, DQ checklists, and reusable modular patterns.

3️⃣ Integrate Executable Logic: Connect your AI agents directly to your data catalogs (like Unity Catalog). Let them query exact schemas and lineage before they write a single line of code to eliminate hallucinations.

4️⃣ Establish Evaluation Trails: Treat AI-generated code just like a junior engineer’s PR. Define rigorous infrastructure rules, log volume changes, and audit implementations over time.