AI & LLM Engineering for .NET Architects

The rise of SLMs: Phi-3, Llama-3-8B, and Mistral

1 Views Updated 5/4/2026

Small Language Models (SLMs)

Bigger is not always better. While GPT-4 has trillion of parameters, Small Language Models (SLMs) like Phi-3 (3.8B parameters) can perform just as well for specific tasks while being 100x cheaper and faster.

1. Why SLMs?

  • Low Latency: Response times are measured in milliseconds, not seconds.
  • Privacy: Can run on-premise without ever sending data to the public cloud.
  • Cost: Running an SLM on your own hardware costs $0 per token.

2. The Microsoft Phi Series

Microsoft's Phi-3 is the world's most capable SLM for its size. It was trained on high-quality "Textbook-grade" data, allowing it to beat models 10x its size in reasoning and logic. For a .NET architect, Phi-3 is the perfect "Utility" model for data cleanup or summarization.

4. Interview Mastery

Q: "When would you use an SLM instead of an LLM?"

Architect Answer: "I use an LLM for complex, creative, or multi-step reasoning where I need the absolute maximum intelligence. I use an SLM for **Known Domains** or **Micro-Tasks** (like intent classification, data formatting, or sentiment analysis). SLMs are also mandatory for 'Edge' scenarios where there is no internet connection, such as AI on a mobile device or in a disconnected industrial factory."

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