Swapping AI Models in a Web Game Exposed Hidden Architectural Flaws

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A developer building a Codenames AI web game encountered unexpected system failures after upgrading from gpt-4o-mini to gpt-5-mini to improve gameplay without raising costs. Rather than simple performance regressions, the model swap triggered validation failures and retry spikes that had never surfaced under the previous model. Investigation revealed the root causes spanned three interdependent layers: prompt contracts, deterministic validators, and downstream consumers. The new model produced structurally valid JSON but violated implicit assumptions, such as including off-board words in target arrays or attaching unsolicited commentary that caused entire candidate batches to be rejected. The episode highlighted that prior model behavior had been quietly masking contract gaps that the architecture had never formally enforced.

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