Making an AI model easy to replace at the API level does not settle the product work involved in replacing it. A Fly.ioproduct-design essay argues that prompts, evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → tests and user expectations become coupled to a model's behavior. A replacement can therefore require retuning the experience even when the connection code changes easily.
The argument is most useful for products whose value depends on predictable interactions. Time spent understanding one model's behavior may improve that experience more directly than supporting many interchangeable providers. The author acknowledges that infrastructure products can have a stronger need for model choice, making the tradeoff dependent on what the product serves.
Terms in this piece · Glossary
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
