Stop building AI demos. Start shipping AI systems that actually work.

Most developers can call an API and write a prompt. Few can build AI systems that consistently deliver production-quality results in enterprise environments. This all-day workshop teaches both.

Morning: Engineer prompts that work. Learn structural patterns and constraint systems that eliminate garbage outputs. No more prompt lottery.

Afternoon: Build production infrastructure. Use gateway architecture (PortKey) to handle multi-LLM failover, A/B testing, governance, and guardrails. The critical production concerns tutorials skip.

Walk away with: A systematic framework for reliable prompts. A production-ready AI gateway. Architecture patterns that pass compliance reviews.

Perfect for: Backend developers, solutions architects, and technical leads shipping AI products in enterprise environments.

Bring: Laptop with ChatGPT API or Claude console account. We focus on engineering that makes AI work when it matters.

Core Learning Modules:

🔧 **Multi-LLM Integration**
- Connect and manage multiple AI providers (OpenAI, Anthropic, Cohere, local models)
- Implement intelligent fallback strategies and load balancing
- Handle rate limits and cost optimization across providers

🧪 **Production A/B Testing**
- Design and deploy model comparison experiments
- Implement traffic splitting for performance evaluation
- Measure and analyze AI system effectiveness in real-time

🛡️ **AI Governance & Compliance**
- Establish organizational AI policies and approval workflows
- Implement audit trails and usage monitoring
- Design cost control and budget management systems

⚡ **Guardrails That Actually Work**
- Build content filtering and safety mechanisms
- Implement input/output validation and sanitization
- Create custom business logic guards and compliance checks

**Who Should Attend:**
- Backend developers moving into AI integration
- Solutions architects designing AI-powered systems
- DevOps engineers responsible for AI infrastructure
- Technical leads planning enterprise AI adoption

**Prerequisites:**
- Basic API development experience
- Familiarity with cloud services (AWS/Azure/GCP)
- Understanding of microservices architecture
- Laptop with Docker installed

**Workshop Outcome:**
Walk away with a production-ready AI gateway configuration, implementation playbooks, and the confidence to deploy enterprise-grade AI systems that your compliance team will actually approve.