AI API questions,
answered directly.

Define the request and response shape, authentication boundary, timeout behavior, and failure states first. Validate model output before it is stored, displayed, or passed to another workflow.

Check the API reference for each model’s supported inputs, output formats, context limits, and tool use. Confirm the exact combination your workflow needs before implementation rather than assuming feature parity.

If structured output is supported by the selected model, define the required schema and validate the returned value before using it. Keep parsing separate from validation so malformed responses do not move downstream.

Review the current plan and model documentation for request units, rate limits, and spend controls. Add server-side rate limits and request timeouts so traffic spikes do not pass through unchecked.

Track latency, error rates, request volume, token usage, and cost signals that apply to the service. Log enough request context to reproduce failures while keeping sensitive inputs and credentials out of client-visible logs.