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 20 of 30 67% of course

Self-Correction Patterns: Letting the AI check its own work

17 · 8 min · 5/23/2026

Sign in to track progress and bookmarks.

Self-Correction Patterns

Even the best AI makes mistakes. But the AI is often smart enough to fix its own mistakes if you ask it to. This is the Self-Correction pattern.

1. Multi-Step Validation

Instead of one prompt, use three:

  1. **Generate:** "Write a Python script for 'X'."
  2. **Review:** "Review the script above for bugs or security holes. List any issues."
  3. **Fix:** "Rewrite the script fixing all the issues found in step 2."
This "Thoughtful" approach yields 40% higher quality code than a single-shot prompt.

2. Format Enforcement

If you need JSON and the AI returns invalid JSON, don't crash. Send the error back to the AI: "Your previous response was not valid JSON. Here is the error: `SyntaxError: Unexpected token`. Please try again." The AI will almost always fix the bracket or comma immediately.

4. Interview Mastery

Q: "What is 'Self-Reflection' in Agentic workflows?"

Architect Answer: "Self-reflection is where the AI maintains a 'Internal Monologue.' It records its own logic: 'I tried to use the Customer API but it returned 0 results. Perhaps the customer ID is wrong. I will try searching by email instead.' This ability to 'Pivot' based on its own failed attempts is what makes an **Autonomous Agent** much more powerful than a simple chatbot."

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
Previous Next
AI & LLM Engineering for .NET Architects

On this page

1. Multi-Step Validation 2. Format Enforcement 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