Use App Configuration for operational switches
Keep runtime switches and adjustable operational settings in Azure App Configuration instead of requiring a deployment for every change.
Reliability
The Model Is Only One Dependency. Map the Rest.
How to map the execution, prerequisite, and operational dependencies around an AI feature and give each interaction a usable failure contract.
Set request body limits intentionally
Choose global and endpoint-specific request body limits instead of inheriting accidental defaults for uploads and APIs.
Use streaming for large or frequent uploads
Use MultipartReader when buffered ASP.NET Core uploads put too much pressure on memory, temporary-disk capacity, or disk I/O.
Calling a Model Is Easy. Running an AI System Is Not
Why a successful model call says little about whether an AI feature is reliable, secure, observable, affordable, and ready to operate in production.
Choose rate-limiting policies by endpoint cost
Apply different ASP.NET Core rate limits to cheap reads and expensive AI or data-processing endpoints.
Design queue handlers for duplicate delivery
Assume a queue message can arrive more than once and make the handler’s business effects safe to repeat.
Validate structured model output before using it
Treat deserialized model output as untrusted data and apply normal business validation before it drives application behavior.
Use PeriodicTimer for cancellable asynchronous loops
Use PeriodicTimer when a background loop needs an awaitable cadence, cooperative shutdown, and sequential executions.
Validate required options at startup
Fail during application startup when required configuration is missing or invalid instead of waiting for the first request.