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 3 of 30 10% of course

Prompt Versioning & Management in Production

14 · 8 min · 5/23/2026

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Enterprise Prompt Management

In production, your prompts are Code. They should not be hard-coded strings in your C# files. They should be versioned, tested, and deployable like any other microservice.

1. Prompts as Artifacts

Store your prompts in a separate repository or a Prompt Management System (like Portkey or LangSmith). This allows your "AI Product Manager" to update a prompt's wording without requiring a full code deployment and restart.

2. A/B Testing Prompts

When you update a prompt, you must run it against a Golden Dataset (a list of known correct answers). If the new prompt improves "Accuracy" for one feature but breaks another, you catch it in CI/CD before the user does.

4. Interview Mastery

Q: "Why is 'Semantic Versioning' important for AI Prompts?"

Architect Answer: "Because even a 1-word change in a prompt can fundamentally change the JSON schema output. We use **SemVer** to track breaking changes in prompts. If I update a prompt to a new version that returns a different object structure, all downstream microservices must know to update their parsers accordingly. Prompts are the 'Contract' of the AI era."

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
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AI & LLM Engineering for .NET Architects

On this page

1. Prompts as Artifacts 2. A/B Testing Prompts 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