1.

What parts of our system (or process) are truly “core and adopted”—and which are still experimental or unproven?

Clarifies where to enforce strict scaling, standards, and review versus where to optimize for learning and speed.

2.

Where have we drawn explicit, non-negotiable principles or “golden lines”—and do all team members know never to cross them, regardless of context or pressure?

Ensures that ethical, security, or compliance boundaries are always rigorously upheld and never subject to marginal-cost rationalization.

3.

How do we ensure we earn the right to scale (harden, invest, or add process) through actual evidence—such as customer adoption, usage metrics, or risk signals—rather than on speculative or theoretical needs?

Prevents premature over-investment and focuses attention on real-world demands.

4.

What are the opportunity costs and trade-offs if we choose to scale, invest, or harden this area right now—and why is “now” the right moment, versus continuing to prioritize learning and adaptability?

Makes sure teams consider what may be sacrificed (speed, learning, resources elsewhere) and directly confront whether scaling is justified at this time.

5.

What tools, practices, and safety nets (e.g., feature flags, rollbacks, observability, experimentation) do we have in place to support rapid iteration in unproven areas, and resilient reliability in proven ones?

Bridges engineering enablement with adaptive process, allowing both agility and safety where each is needed.

6.

How do we regularly revisit (and communicate) whether a given component, process, or principle is in the “learn fast” or “scale for reliability” zone—and are we willing to shift our approach as facts and adoption change?

Promotes a culture of reflection and adaptation, rather than rigid dogma or endless chaos.

Perplexity AI: Ask Anything