Testing & Switching Models

Evaluation

Part of: Choosing the Right Model

Here's the question that ends most model debates: "Did you actually test it?" Opinions about which model is best are cheap. A tiny eval set : a handful of real tasks with known good answers, turns the argument into a measurement. Run your candidates, compare the numbers, pick the winner. Then switching is just changing one model id. What it is An eval (evaluation) is a small set of test cases you run every candidate model against, then score on what you care about: quality, cost, latency. It doesn't need to be fancy. Ten representative tasks with expected answers beats a thousand vague vibes. The companion idea is model-agnostic code : your app shouldn't hardcode one model deep inside its logic. Keep the model id in one place (a variable or config) so swapping models is a one-line change, not a treasure hunt. How it works You loop your candidates over the eval set and tally results: The crucial detail: model id is a parameter . To try a new model you add it to the list, you never rewrite run model. That's model-agnostic code in action. Why it matters Evals turn model choice from faith into evidence: - You can defend the decision. "Small scored 9/10 at one-tenth the cost" beats "the

Challenge: Eval Harness