How to Build AI Systems That Stay Robust as Models Come and Go

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A software engineer writing for DEV Community shares lessons from building production AI systems, emphasizing that applications should be designed around capabilities rather than specific models. Tightly coupling code to a single provider like GPT-4 turns routine model switches into costly refactoring efforts, which can be avoided by routing all AI calls through a provider-agnostic interface. The author also argues that prompts deserve the same engineering discipline as source code, including versioning, peer review, and documented change history. New models should be rolled out gradually using feature flags, with monitoring for latency, cost, and output quality before full deployment. Rather than relying on public leaderboards, teams are advised to build their own evaluation datasets and measure what actually matters for their specific use case.

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