LLM API Integration Patterns: Building Reliable AI-Powered Applications
Integrating LLM APIs into production applications requires more than just making API calls. These patterns address the real challenges: rate limits, token costs, latency, and reliability. Basic Client Setup 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 import os from anthropic import Anthropic client = Anthropic( api_key=os.environ.get("ANTHROPIC_API_KEY"), timeout=60.0, max_retries=3, ) def chat(message: str, system: str = None) -> str: """Simple completion with sensible defaults.""" messages = [{"role": "user", "content": message}] response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, system=system or "You are a helpful assistant.", messages=messages, ) return response.content[0].text Retry with Exponential Backoff Built-in retries help, but custom logic handles edge cases: ...