After extensive building with AI, I can see my early mistakes clearly. Maybe sharing them helps you skip some pain.
Wrong: AI Is About Output Speed
I thought: AI lets you produce more, faster. The value is velocity.
Reality: AI produces unlimited output. Velocity isn't the constraint anymore. Understanding is.
The shift: optimize for understanding, not speed. Document as you build. Pause to comprehend. Speed without understanding is just faster chaos.
Wrong: Documentation Is Overhead
I thought: documentation is what you do after shipping, if there's time. It's overhead that slows you down.
Reality: in AI development, documentation is often the most valuable output. Code is regenerable. Context is not. The documentation is what persists.
The shift: documentation isn't after the work. It's during the work. Intent before. Decisions during. Evidence after. This is the primary output.
Wrong: Prompts Are Throwaway
I thought: prompts are just inputs. Write them, get output, move on. The output is what matters.
Reality: prompts encode thinking. They represent accumulated learning. They're intellectual property worth protecting.
The shift: treat prompts like code. Version them. Document them. Organize them. Protect them.
Wrong: Traditional Process Still Works
I thought: Agile/Scrum adapts to everything. Just run the same process with AI added.
Reality: AI changed the constraints. Practices designed for slow coding create friction when coding is fast. The ceremonies don't fit.
The shift: question every process element. Does this address a real constraint we have now? If not, cut it. If yes, keep it. Match process to actual constraints.
Wrong: More People = More Better
I thought: scale the team to scale the output. Coordination overhead is the price of capacity.
Reality: AI changes the math. Small teams with AI accomplish what large teams did before. Coordination overhead doesn't shrink automatically.
The shift: small teams are often better. Question whether you need the headcount. Reduce coordination overhead where possible.
Wrong: Decisions Can Wait
I thought: make decisions when you have to. Document them when you have time. Later is fine.
Reality: context decay is fast. By "later," the reasoning is fuzzy. Decisions documented later are documented poorly or not at all.
The shift: capture decisions when made. 30 seconds of documentation now beats 30 minutes of archaeology later.
Wrong: AI Remembers
I thought: the AI would somehow accumulate context. It would learn my patterns.
Reality: each session starts fresh. AI has no memory between sessions. The "memory" has to come from me.
The shift: context preservation is my job. Capture at end of sessions. Load at start of sessions. Create systems that persist what AI cannot.
What I Do Now
Based on these lessons:
1. **Intent before action.** Articulate what I'm trying to accomplish before I start prompting.
2. **Document as I go.** Decisions when made. Context while fresh. Not after.
3. **Treat prompts as IP.** Version. Document. Organize. Protect.
4. **Question process.** Every ceremony and practice justified on its own merits, not by habit.
5. **Keep teams small.** Minimize coordination overhead. Trust AI to amplify small teams.
6. **Preserve context.** End of session capture. Start of session loading. Systems for persistence.
7. **Measure understanding.** Not velocity. Can we explain what we built? That's the metric.
These aren't earth-shattering insights. But they represent real shifts from where I started. Hopefully they save you some learning time.
The biggest shift: recognizing that AI changed the game, not just sped up the old game. Different constraints require different practices. Adopting AI without updating how you work is missing the point.