After building extensively with AI, these six principles now guide everything I do. They're the difference between AI-assisted chaos and AI-native effectiveness.
1. Intent Before Automation
Don't start prompting until you're clear on what you want.
Most people: vague idea → prompt → iterate → settle for something
Better: articulate intent → define success → then prompt → evaluate against criteria
The intent articulation step feels slow. It saves far more time than it costs. Vague intent produces vague results and aimless iteration.
**Practice:** Before every AI session, write one sentence: "I'm trying to accomplish X, and success looks like Y."
2. Prompts Are IP
Your prompts encode your thinking. They represent accumulated learning. They're competitive advantage.
Treat them accordingly: - Version them - Document them - Protect them - Organize them into a library
Don't let prompts disappear into chat logs. They're valuable. Act like it.
**Practice:** Save every prompt that works well. Any format, any system. Just capture it.
3. Decisions Are Liabilities
Every decision creates future maintenance burden. Someone will need to understand, maintain, and eventually change what was decided.
Treat decisions as liabilities: - Make fewer decisions - Prefer reversible decisions - Document decisions to reduce carrying cost - Assign ownership
**Practice:** After significant decisions, spend 60 seconds writing: what was decided, why, who owns it.
4. Risk Must Be Explicit
AI systems fail in novel ways. Confident wrongness. Drift. Encoded assumptions. Edge case chaos.
These risks don't announce themselves. They compound silently.
Make them explicit: - Name the risks - Document them - Assign owners - Monitor
**Practice:** For AI systems, list three risks specific to AI behavior. For each: what could happen, how would we know.
5. Memory Is Infrastructure
Teams forget faster than they learn. Without systems to preserve knowledge, you pay full tuition on every project.
Memory isn't nice-to-have. It's infrastructure: - Document decisions when made - Capture learning after projects - Build prompt libraries - Create onboarding materials
**Practice:** After completing something, take 5 minutes: what did we learn that should survive?
6. Learning Must Compound
Each project should make the next one smarter. If lessons evaporate between efforts, you're stuck in a loop.
Build compounding: - Capture evidence of outcomes - Connect evidence to decisions - Extract patterns - Reference past learning when making new decisions
**Practice:** Before major decisions, check: have we faced this before? What happened?
The Interconnection
These principles reinforce each other:
Intent clarity enables better prompts. Better prompts become IP worth protecting. Decisions documented reduce liability. Risks documented get managed. Memory infrastructure preserves all of the above. Learning compounds from preserved knowledge.
It's not six independent practices. It's a coherent orientation toward building with AI.
Getting Started
You don't need to adopt all six immediately.
Pick one. The one where you feel the most pain. Practice it until it's habitual.
Then add another.
Over months, the principles become how you work. The AI chaos becomes AI effectiveness.