API Design for AI Applications: Patterns and Best Practices

Pattern 1: Streaming Responses

Return tokens as they're generated rather than waiting for the complete response. Use Server-Sent Events (SSE) or WebSockets. Dramatically improves perceived performance.

Pattern 2: Async Processing for Long Tasks

• Client submits request → gets a job ID immediately
• Worker processes the AI task in the background
• Client polls job status, or receives webhook when complete

Pattern 3: Idempotency Keys

Accept an idempotency key in the request header. Cache the result for 24 hours. Return the cached result on duplicate requests — prevents running expensive LLM calls twice on retries.

Pattern 4: Token-Based Rate Limiting

Don't just rate-limit by requests per minute — limit by token budget. Track token usage per user/tenant and enforce budgets.

Pattern 5: Versioned Prompts

Version your prompts (v1, v2) and allow clients to pin to a version. Your prompt is part of your API contract — changes break users' expectations.

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