AI & LLM Engineering for .NET Architects

Punitiveness & Bias: Evaluating and mitigating model behavior

1 Views Updated 5/4/2026

AI Ethics: Bias & Fairness

LLMs are trained on the internet, which means they inherit all of humanity's Biases. If your app is used for hiring or loan approvals, those biases can lead to illegal and unethical discrimination.

1. Types of AI Bias

  • Stereotyping: Associating certain jobs or traits with specific genders or ethnicities.
  • Representation Bias: Failing to understand cultures or languages that were under-represented in the training data.

2. Mitigation: Debias Prompts

You can proactively tell the AI to be fair: "Evaluate this candidate based ONLY on their skills and experience. Do not consider their name, gender, or location in your decision."

3. Evaluation Frameworks

Use tools like **G-Eval** or **Deeval**. These are frameworks where you use a HIGH-END AI (GPT-4) to grade a SMALLER AI on its fairness, helpfulness, and bias. This "AI-grading-AI" approach is the only way to scale testing to millions of messages.

4. Interview Mastery

Q: "What is 'AI Red-Teaming'?"

Architect Answer: "Red-teaming is an adversarial test where we hire security experts to 'Attack' our AI app. They try to make it say racist things, reveal secrets, or provide dangerous medical advice. The goal is to find the breaking points *before* the public does. It's a mandatory step for any enterprise-grade AI release."

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