Production AI Engineering

What You'll Learn

• Implement resilience patterns for AI service calls
• Secure AI applications against prompt injection and data leakage
• Set up observability with OpenTelemetry for AI workloads
• Optimize costs across Azure OpenAI deployments
• Design scalable AI architectures for production traffic

Prerequisites

• C# and .NET 8+
• Experience building AI features (see Semantic Kernel or Agents paths)
• Azure subscription

Learning Path Articles

1. 1

   Securing .NET AI Apps: Key Vault, PII Filtering & Prompt Injection Defense

   Production security for .NET AI apps: Key Vault for secrets, PII redaction middleware, content filtering, and prompt injection defense patterns with code.

   Intermediate 12 min read university
2. 2

   AI Workflow Patterns in .NET: Chaining, Fan-Out, Human-in-the-Loop & Agents

   Design reliable AI workflows in .NET: prompt chaining, parallel fan-out, human-in-the-loop, and agent orchestration. Pattern code + when to use each one.

   Intermediate 13 min read university
3. 3

   OpenTelemetry for .NET AI Apps

   Trace .NET AI apps with OpenTelemetry. Track Semantic Kernel calls, token costs, latency, and export telemetry to Aspire and App Insights.

   Intermediate 14 min read university
4. 4

   Cut Azure AI Costs in C#: Token Budgets, Model Routing & Semantic Cache

   Reduce Azure AI Foundry spend in .NET 10: GPT-5.4 tier routing, semantic caching, token budgets, and Ollama for dev. April 2026 pricing examples included.

   Intermediate 15 min read university
5. 5

   Azure.AI.OpenAI v2 Migration: Fix Every C# Break

   Migrating to Azure.AI.OpenAI v2? Map old C# types to ChatClient, ChatMessage, streaming, tools, and compile-error fixes.

   Intermediate 13 min read university