AI / Core OpenAI Codex Application Fundamentals Interview Questions
What are the key considerations for building production-grade OpenAI applications?
Moving from a prototype to a production OpenAI application requires addressing several concerns that do not arise during development: reliability, cost control, observability, and safety.
| Area | Key considerations |
|---|---|
| Cost control | Set spending limits; use Batch API for bulk; choose right model per task; track usage per user |
| Rate limits | Implement exponential backoff; design for asynchrony; use Batch API to sidestep synchronous limits |
| Observability | Log all prompts and responses; use Agents SDK tracing; monitor token usage and latency |
| Safety | Run Moderation API on user inputs; implement guardrails; require approval for irreversible actions |
| Reliability | Implement retries; handle timeouts; have fallback models; test with evals |
| Latency | Use streaming for interactive UIs; prompt caching for repeated prompts; choose model/effort for task |
| Privacy | Understand data handling for your tier; use project API keys; implement data minimisation |
| Versioning | Pin model versions (avoid -latest aliases in production); run evals before model upgrades |
# Production patterns: # 1. Never use -latest model aliases in production # BAD: client.responses.create(model="gpt-5-latest", ...) # behaviour changes without notice # GOOD: client.responses.create(model="gpt-5.5", ...) # stable, predictable # 2. Set both usage limits AND per-request caps: client.responses.create( model="gpt-5.5", input=user_message, max_output_tokens=2048, # prevent runaway outputs timeout=30.0, # prevent hanging requests ) # 3. Log everything for debugging: import logging logger = logging.getLogger("openai-app") response = client.responses.create(model="gpt-5.5", input=prompt) logger.info({ "model": "gpt-5.5", "input_tokens": response.usage.input_tokens, "output_tokens": response.usage.output_tokens, "response_id": response.id, })
Why should you avoid using model aliases ending in '-latest' (e.g. 'gpt-5-latest') in production?
What is the recommended approach when you need to upgrade an OpenAI model in production?
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