1. Error Handling & Retries
AI APIs can fail due to rate limits, overload, or network issues. Implement exponential backoff with jitter for retries. Always handle specific error codes (429 for rate limits, 500 for server errors).
python
import time
from tenacity import retry, wait_exponential, stop_after_attempt
@retry(wait=wait_exponential(min=1, max=60), stop=stop_after_attempt(5))
def call_api_with_retry(prompt):
return client.chat.completions.create(...)2. Cost Management
Monitor token usage closely. Use smaller models (GPT-3.5, Claude Haiku) for simple tasks. Cache common responses. Set max_tokens limits. Consider batching requests for bulk operations.
3. Performance Optimization
Use streaming for better perceived latency. Implement request queuing for high-volume applications. Consider running inference in parallel when tasks are independent.
Wrap-up
Production AI applications need the same engineering rigor as any critical system. Invest in monitoring, alerting, and graceful degradation.