The API call is the easy part. The wrapper is where engineering maturity starts.
Production starts where "just call the API" stops. Between your application and the model, a real system usually needs a wrapper layer.
The raw API only gives you the call. It does not give you the operating discipline around the call.
Versioned prompt templates, A/B testing, variable injection — not hardcoded strings
Simple queries → GPT-4o-mini (fast, cheap). Complex reasoning → GPT-4o or Claude. Fallback if primary fails.
Input validation (PII detection, prompt injection filtering). Output validation (schema match?). Token budget enforcement.
Every call logged: prompt, response, latency, tokens, cost. Quality metrics tracked. Drift detection.
Identical queries → cached response. Semantic similarity caching. 40-60% fewer API calls in production.
Respect API limits. Exponential backoff. Queue management for batch processing.
Most production failures are not caused by the model being magical or not magical enough. They come from weak system design around it:
Design insight: If someone says they 'put an LLM into production,' the next question is not 'Which model?' It is: 'What sits between your app and the model?' That answer tells you whether they have a demo, or a system.