How to Use GPT-6 Astra to Compare Two Game Prototypes Before You Commit to One
Compare prototypes against the same playable criteria instead of asking which one is “better.” Lock the test scenario, score both on feel, clarity, failure rate, implementation risk, and iteration cost, then keep the evidence separate from preference.
Compare prototypes against the same playable criteria instead of asking which one is “better.” Lock the test scenario, score both on feel, clarity, failure rate, implementation risk, and iteration cost, then keep the evidence separate from preference.
Make both prototypes answer the same design question
Playco's published Astra workflow used multiple playable prototypes so developers could compare ideas rather than imagine them. The useful pattern is to define one question—such as which dash mechanic creates clearer risk/reward—and make each prototype differ only where it needs to answer that question.
Use a shared evaluation sheet
- Time to understand the core loop.
- Input responsiveness and control clarity.
- Failure cases or confusing states observed during play.
- Performance or engine warnings.
- Amount of custom code and hidden complexity.
- Expected cost of the next three iterations.
Do not let the model hide preference inside the score
Have Astra record observations first, then score. “Prototype B feels better” is not evidence; “B produces fewer overshoots at the same input speed and needs one state transition instead of three” is. Keep subjective design preference in a separate final decision section.
Commit only after the losing prototype teaches you something
Before deleting the alternative, capture any mechanic, parameter range, failure mode, or architecture lesson worth carrying forward. The point of parallel prototyping is not just choosing a winner; it is buying information cheaply before the project hardens.
Working template
DESIGN QUESTION: [single question] PROTOTYPE A: [what differs] PROTOTYPE B: [what differs] TEST SCENARIO: [same for both] MEASURE: [feel/clarity/failures/perf/complexity] OUTPUT: observations first, score second, recommendation last; clearly label subjective judgments.
Source and verification note
This guide was checked against OpenAI / Playco case study and updated Sep 5, 2026. Product behavior can change; current primary documentation should take precedence over this guide.