How to Build AI Features Before Your Preferred Model Is Available

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When a highly anticipated AI model is announced but not yet publicly available, developers face a dilemma: wait and stall, or build on a lesser model and refactor later. A practical approach treats the model as a swappable dependency, abstracting all model-specific logic into a single function so that switching models requires only a configuration change. Developers are advised to build a standardised test set of real, edge-case inputs to objectively evaluate any new model against the current one upon release. Per-model feature flags allow gradual traffic rollouts, reducing the risk of a full commitment before performance is confirmed. Transparent UI communication is also stressed — interfaces should clearly indicate which model is actually being used rather than implying unreleased capabilities are already live.

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