By name: ai-exponential-pm-shifts description: A practical workflow guide for product teams operating under rapid model capability gains: short sprint exploration, demo/eval-driven discovery, revisiting features, and favoring simple implementations.
Instructions
Use this workflow when model capability is improving quickly and you want your product process to keep up.
Plan in short sprints (run side quests)
- Encourage short, self-directed experiments outside the main roadmap.
- Treat an afternoon prototype or capability test as a first-class output.
Prefer demos and evals over docs
- Build a rough prototype early to make the conversation concrete.
- Create lightweight evals to measure whether the feature works and where it fails.
Revisit features with new models
- Be a daily active user and deliberately test tasks you assumed were too hard.
- If users are stitching together manual workflows, treat that as scaffolding to productize.
Do the simple thing that works
- Avoid clever workarounds that only exist to compensate for current model limits.
- Favor simple implementations that become easier to upgrade as models improve.
Optimize for capability first
- Don’t cut token usage too early; first confirm the feature is possible and valuable.
Examples
Example: Turn a written spec into a prototype
Use the template below as a starting point:
templates/spec-to-prototype.md
Example: Create a small eval set for a risky feature
Start with:
examples/lightweight-eval-starter.md
Example: Daily active testing prompt
“Try doing the thing we assumed was too hard. If it works, suggest how we should productize the manual steps users are doing today.”