Developer checklist for AI API tools

An AI API can look excellent in a playground and still be hard to operate. Check timeout behavior, streaming stability, structured error codes, and whether retries can be made safely.

Billing and rate-limit clarity matter because usage can grow quickly once an AI workflow becomes part of production.

For production workloads, log prompt version, model, latency, token usage, response status, and downstream failures. Without that trail, debugging quality regressions becomes guesswork.