Backend API Design and Request Lifecycle
An AI endpoint should authenticate the caller, validate request size and shape, apply quotas, load permission-scoped context, invoke the model, validate output, and return a controlled response. Keep model calls out of client-side code when they require secrets or privileged data. Define request IDs, deadlines, cancellation, and response schemas.
Use synchronous endpoints for short interactive requests and queues for long ingestion, batch classification, or multi-step workflows. A job API should return a job ID and expose status/progress with authorization checks. Avoid holding an HTTP request open for work that may exceed platform timeouts.
Request lifecycle
POST /api/ai/answer
-> authenticate
-> validate + rate-limit
-> retrieve authorized context
-> call model with deadline
-> validate output
-> persist minimal audit event
-> return response💡 Each step should have explicit failure handling and timing. Return safe error messages to clients while retaining enough internal telemetry to diagnose the underlying failure.
Key Takeaways
- Authenticate and validate before provider invocation.
- Use deadlines and cancellation.
- Use durable jobs for long-running tasks.
Knowledge Check
When is a durable background queue preferable to a long synchronous HTTP request?