Choose the model for the job.
A model strategy begins with the work you need completed. Match generative, coding, and decision-oriented outputs to a tested contract, then budget the real workflow rather than an isolated demo.
Evaluate against your own examples
Use representative inputs, including incomplete and ambiguous cases. Define success in terms your team can review. Keep a separate evaluation set so repeated prompt tuning does not create an overly optimistic picture of reliability.
Make tokens and budgets observable
Track input size, provider-reported usage, retries, and latency by task. Review current provider pricing separately from your measurements. Do not turn a vendor benchmark or a remembered token price into a promise about your own application’s performance or cost.
Keep fallback behavior explicit
When a provider fails or returns an invalid response, decide whether the workflow stops, retries within a budget, or requests review. A fallback should not silently expand the system’s permissions. Keep model identifiers and policy revisions in the task record.