AI & LLM Engineering for .NET Architects
1. AI Foundations & Prompt Engineering
The LLM Landscape: Transformers, Attention, and Tokens Advanced Prompt Engineering: Few-shot, Chain-of-Thought, and ReAct Prompt Versioning & Management in Production LLM Cost Estimation: Token accounting and budget strategies
2. Semantic Kernel & Integration
Introduction to Microsoft Semantic Kernel (SK) Skills & Plugins: Extending the LLM with native C# functions Planner & Orchestration: Automating complex multi-step AI tasks Connectors: Switching between OpenAI, Azure OpenAI, and HuggingFace
3. Vector Databases & RAG
The RAG Pattern: Solving the 'Static Knowledge' problem Embeddings Deep Dive: Converting text to math Vector DBs: Azure AI Search vs Pinecode vs Milvus Hybrid Search: Combining Keyword and Semantic search for accuracy
4. Advanced RAG Techniques
Document Chunking Strategies: Overlap, Slidewindow, and Semantic splitting Recursive Document Processing for massive knowledge bases Context Window Management: Summarization vs Truncation Citations & Grounding: Ensuring the AI doesn't hallucinate
5. AI Safety & Guardrails
Content Moderation: Azure AI Content Safety integration Prompt Injection: Defending against adversarial attacks Punitiveness & Bias: Evaluating and mitigating model behavior Self-Correction Patterns: Letting the AI check its own work
6. Small Language Models (SLMs) & Local AI
The rise of SLMs: Phi-3, Llama-3-8B, and Mistral Running AI Locally with ONNX and LocalLLM Quantization: Running 70B models on 16GB RAM Edge AI: Deploying models to local devices and private clouds
7. Multimodal & Agentic AI
Multimodal AI: Processing Images, PDFs, and Audio in C# Agentic Workflows: Multi-agent collaboration with AutoGen Function Calling: Letting the LLM use your SQL and API tools Memory Management: Ephemeral vs Long-term Semantic memory
8. FAANG AI Engineer Interview
Case Study: Designing a Global Enterprise AI Knowledge Assistant Case Study: Building an Autonomous AI Agent for Software Dev
AI & LLM Engineering for .NET Architects

Connectors: Switching between OpenAI, Azure OpenAI, and HuggingFace

1 Views Updated 5/4/2026

AI Connectors: Avoiding Lock-in

The AI world moves fast. GPT-4 is the king today, but Llama or Claude might be the king tomorrow. Using Connectors ensures your app is future-proof.

1. Azure OpenAI (Enterprise Grade)

Allows you to use OpenAI models inside your own Azure tenant. Your data never leaves your private cloud and is never used to train the model. This is the only way to get AI through an Enterprise security review.

2. HuggingFace & Local Models

Using the HuggingFace connector, you can talk to thousands of specialized open-source models. For sensitive data, you can use local connectors like **Ollama** or **LlamaSharp** to run models entirely on your own GPU/CPU with zero internet connection.

3. Semantic Kernel DI integration

You can define your connectors in appsettings.json and inject them into the kernel. This allows you to use a cheap local model for testing and a high-end Azure model for your paying customers without changing your code logic.

4. Interview Mastery

Q: "How do you handle 'Load Balancing' across multiple AI instances?"

Architect Answer: "We use a **Gateway/API Proxy** pattern. If the US-East Azure OpenAI instance has a quota of only 200 requests per minute, our SK connector can be configured to 'Failover' to US-West or Europe-North instances automatically. This ensures high availability even during regional AI outages or rate-limit spikes."

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AI & LLM Engineering for .NET Architects
1. AI Foundations & Prompt Engineering
The LLM Landscape: Transformers, Attention, and Tokens Advanced Prompt Engineering: Few-shot, Chain-of-Thought, and ReAct Prompt Versioning & Management in Production LLM Cost Estimation: Token accounting and budget strategies
2. Semantic Kernel & Integration
Introduction to Microsoft Semantic Kernel (SK) Skills & Plugins: Extending the LLM with native C# functions Planner & Orchestration: Automating complex multi-step AI tasks Connectors: Switching between OpenAI, Azure OpenAI, and HuggingFace
3. Vector Databases & RAG
The RAG Pattern: Solving the 'Static Knowledge' problem Embeddings Deep Dive: Converting text to math Vector DBs: Azure AI Search vs Pinecode vs Milvus Hybrid Search: Combining Keyword and Semantic search for accuracy
4. Advanced RAG Techniques
Document Chunking Strategies: Overlap, Slidewindow, and Semantic splitting Recursive Document Processing for massive knowledge bases Context Window Management: Summarization vs Truncation Citations & Grounding: Ensuring the AI doesn't hallucinate
5. AI Safety & Guardrails
Content Moderation: Azure AI Content Safety integration Prompt Injection: Defending against adversarial attacks Punitiveness & Bias: Evaluating and mitigating model behavior Self-Correction Patterns: Letting the AI check its own work
6. Small Language Models (SLMs) & Local AI
The rise of SLMs: Phi-3, Llama-3-8B, and Mistral Running AI Locally with ONNX and LocalLLM Quantization: Running 70B models on 16GB RAM Edge AI: Deploying models to local devices and private clouds
7. Multimodal & Agentic AI
Multimodal AI: Processing Images, PDFs, and Audio in C# Agentic Workflows: Multi-agent collaboration with AutoGen Function Calling: Letting the LLM use your SQL and API tools Memory Management: Ephemeral vs Long-term Semantic memory
8. FAANG AI Engineer Interview
Case Study: Designing a Global Enterprise AI Knowledge Assistant Case Study: Building an Autonomous AI Agent for Software Dev