Not the model. The workflow.
Models change quickly. Value lives in context, rules and a workflow built to survive the change.
AI discussions often revolve around models. Which one is fastest? Which one solves the hardest benchmark? Which one has the largest context window? These are interesting questions. For a working product, however, they are rarely the most important ones.
Context carries the system
A model does not know my use case, my users or the rules of my product. The system has to provide all of that: relevant data, clear boundaries, an understandable objective and a defined shape for the result.
That is why the conversation is increasingly shifting from prompt engineering to context engineering. A well-written sentence in an input field is not enough. What matters is which information is available at which moment, how state is preserved and what happens when an answer is incomplete, contradictory or simply wrong.
A robust workflow makes the model replaceable. If changing the model breaks the entire product, the model was probably not just one component. It had quietly become the architecture.
A prototype is not a product
Webwoven was a useful example for me. AI helped me move from ideas to implementation faster and build a playable prototype during Build Week. The core game logic remained deterministic. The same rules need to produce understandable results. A game rule cannot change merely because a model responds differently today than it did yesterday.
The same separation applies beyond games. AI can work with ambiguous information, make suggestions or structure content. Permissions, billing, state transitions and other critical processes need explicit rules.
Production readiness begins where the demo ends: with security, tests, observable behavior, understandable errors and a deployment process that remains under control. Vibe coding can be an excellent starting point. It gives more people access to software development and makes ideas visible quickly.
My goal, however, is not the moment when something works for the first time. My goal is a system that remains understandable and reliable the hundredth time it runs. For that, the workflow usually matters more than the model.