Best Practices for Production AI APIs

Production Best Practices

Shipping AI to production is different from running it in development. Here are the battle-tested best practices we've learned serving AI APIs at scale.

1. Never Trust User Input

AI systems are particularly vulnerable to prompt injection. Always use UnforgeAPI's strict_mode to block malicious inputs.

2. Implement Graceful Degradation

AI APIs can fail. Plan for it with proper error handling for rate limits, server errors, and client errors.

3. Use Streaming for Better UX

Don't make users wait for complete responses. Enable streaming so users see responses appearing in real-time.

4. Cache Aggressively

Many AI queries are repetitive. Caching can reduce API costs by 30-50% for typical applications.

5. Implement Rate Limiting

Protect your API and budget with per-user rate limits.

6. Log Everything (Anonymized)

Logs are essential for debugging and improvement. Log duration, routing path, grounding status, and confidence—but not PII.

7. Set Timeouts

AI responses can sometimes take a while. Set appropriate timeouts (30s is a good default).

8. Validate Responses

Don't blindly trust AI output. Check grounding status and confidence scores before returning to users.

9. Use Webhooks for Long Operations

For complex queries, use async processing with webhooks for completion notifications.

10. Monitor and Alert

Set up monitoring for key metrics:

• Latency: Alert if p95 > 5s
• Error rate: Alert if > 1%
• Grounding rate: Alert if < 80%
• Usage: Alert if approaching limits

Summary

Production AI is about reliability, not just capability:

1. Secure: Validate inputs, use strict_mode
2. Resilient: Handle failures gracefully
3. Fast: Stream, cache, timeout
4. Observable: Log, monitor, alert
5. Trustworthy: Validate outputs, check grounding

Follow these practices and your AI integration will be production-ready.