Building production AI applications requires thoughtful architecture. Here are the most important patterns and considerations.

Common Architecture Patterns

1. Simple Proxy The most basic pattern: your backend proxies requests to an AI API.

Client → Your Backend → AI API → Your Backend → Client

Best for: chatbots, simple Q&A, content generation features.

2. RAG (Retrieval-Augmented Generation) Combine your data with AI for accurate, grounded responses.

Client → Backend → Vector DB (retrieve) → AI API (generate with context) → Client

Best for: customer support, documentation Q&A, internal knowledge bases.

3. Agent Loop AI decides what actions to take in a loop until the task is complete.

Client → Backend → AI (decide action) → Execute Tool → AI (evaluate) → ... → Client

Best for: complex multi-step tasks, data analysis, workflow automation.

4. Pipeline Chain multiple AI calls together, each transforming the output.

Input → Classify → Extract → Validate → Format → Output

Best for: document processing, content moderation, data enrichment.

Security Essentials

• Never expose API keys — All AI calls go through your backend
• Input validation — Sanitize user inputs before sending to AI
• Output filtering — Check AI responses for harmful content before returning
• Prompt injection defense — Separate system instructions from user input; validate AI actions
• Rate limiting — Per-user and per-IP limits to prevent abuse and cost overruns

Testing AI Applications

AI outputs are non-deterministic, which makes testing challenging:

• Use temperature=0 for reproducible test cases
• Build evaluation datasets with expected outputs
• Test for safety with adversarial inputs
• Monitor output quality metrics in production
• Use A/B testing for prompt and model changes

Monitoring and Observability

Track these metrics in production:

• Latency (p50, p95, p99)
• Token usage and costs per user/feature
• Error rates by type
• User satisfaction signals (thumbs up/down, regenerations)
• Content safety flags

Course Conclusion

You now have the knowledge to build production AI applications: from basic API calls to structured outputs, function calling, multimodal capabilities, and robust architecture patterns. The AI API ecosystem is evolving rapidly — keep experimenting, start building, and ship! 🚀