Repeatable Inference Operations

Control the variables

Record the exact model tag (its name and version), reusable prompt template, supplied context, generation settings, and application version. Temperature is a setting that changes how adventurous token choices are. Lower values usually make extraction and tests more repeatable; higher values can produce more variety.

Set a timeout so a model call cannot run forever. Limit both input and output length. Keep concurrency - the number of requests running at the same time - low on CPU-only hosts. A health check proves the daemon answers at all. A stricter readiness check can also prove that the required model is installed before the service accepts work.

Scenario: An API passes its process health check, but every request fails because the configured model tag was never installed. A useful readiness check catches the inventory mismatch before traffic arrives.

Goal: Repair the wrong-model incident in the graded AI lab by correcting configuration and proving the tiny approved model serves requests.