Syllabus

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
Lesson 24 of 30 80% of course

Edge AI: Deploying models to local devices and private clouds

16 · 8 min · 5/23/2026

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AI at the Edge

The cloud is not everywhere. Edge AI is about bringing the power of the LLM to the device where the data is born—whether that's a phone, a factory sensor, or a private server in a hospital.

1. The "Cloud-Cloud" vs "Edge-Cloud" Hybrid

Modern architecture uses a hybrid approach:

  • Edge: Performs sensitive data filtering, PII removal, and basic intent detection.
  • Cloud: Only receives the 'Clean' data for high-end reasoning if the Edge can't handle it.
This saves bandwidth and ensures maximum data privacy.

2. Private AI Clusters

Enterprises are building "Local AI Clusters" using tools like **Ollama** or **vLLM** hosted in their own Kubernetes clusters. This gives them a private GPT endpoint that their internal developers can use without the data ever touching the public internet.

4. Interview Mastery

Q: "What is 'Latency Sensitive' AI?"

Architect Answer: "Latency-sensitive AI is where a 1-second delay is unacceptable (e.g., self-driving cars or real-time translation). For these, we must use **In-Process Inference**. We compile the ONNX model directly into our C# binary. By avoiding the 'Network Roundtrip' to a cloud API, we reduce the response time from 1,500ms to <20ms."

Interview Q&A for this course

Test your knowledge

Quizzes linked to this course—pass to earn certificates.

RAG-based Search System Project — Practice Quiz
Test your understanding of RAG-based Search System Project. Retrieval-Augmented Generation…
Advanced 15 min
Prompt Engineering — Practice Quiz
Test your understanding of Prompt Engineering Tutorial. Write effective prompts for LLMs a…
Intermediate 15 min
Multi-Agent System Project — Practice Quiz
Test your understanding of Multi-Agent System Project. Orchestrate multiple AI agents for …
Advanced 15 min
ML.NET — Practice Quiz
Test your understanding of ML.NET Tutorial. Machine learning inside .NET applications.
Intermediate 15 min
Chatbot with Memory Project — Practice Quiz
Test your understanding of Chatbot with Memory Project. Stateful chatbot with conversation…
Advanced 15 min
AI Fundamentals — Practice Quiz
Test your understanding of AI Fundamentals Tutorial. ML concepts, neural networks intro, a…
Intermediate 15 min
Browse all quizzes
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AI & LLM Engineering for .NET Architects

On this page

1. The "Cloud-Cloud" vs "Edge-Cloud" Hybrid 2. Private AI Clusters 4. Interview Mastery
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