Before coding a complex AI project, validate the plan by presenting it to diverse AI models (including different families like Chinese open models). Use their critical feedback to identify blind spots, challenge assumptions, and refine the core concept before committing to development.
Structure your AI development workflow by matching tools to task complexity. Use powerful, expensive models for core work, but switch to cheaper, faster, or free models for smaller tasks and quick fixes to optimize both cost and development speed.
The 'move fast and break things' mantra is counterproductive for complex AI development. Tools and philosophies prioritizing correctness and thoughtful architecture over raw speed are better suited for building meaningful, non-trivial AI features that don't become overwhelming to manage.