Integrating LLMs into Production Applications

Large Language Models have revolutionized how we build intelligent applications. Here's how to integrate them effectively.

Choosing the Right Provider

When selecting an LLM provider, consider:

• Cost per token for your expected usage
• Rate limits and scaling options
• Latency requirements for your use case
• Model capabilities (context window, reasoning)

Implementation Pattern

from openai import AsyncOpenAI
from tenacity import retry, stop_after_attempt, wait_exponential

client = AsyncOpenAI()

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
async def generate_response(prompt: str) -> str:
    response = await client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=1000
    )
    return response.choices[0].message.content

Cost Optimization Strategies

1. Cache common responses using Redis
2. Use streaming for better UX and perceived performance
3. Implement token counting to prevent runaway costs
4. Consider smaller models for simpler tasks

Error Handling

Always implement proper fallbacks and graceful degradation when LLM calls fail.