Guided course · AI and Career Skills for .NET Developers
AI/LLM Integration in .NET
Add LLM features to .NET applications the way production teams do: Microsoft.Extensions.AI and IChatClient, middleware for caching, telemetry and budgets, streaming over SSE with cancellation, function calling, structured output with JSON schema, embeddings and RAG with Microsoft.Extensions.VectorData (in-memory, with Azure AI Search and pgvector explained), hybrid search with RRF and citations, Microsoft Agent Framework agents and sessions, evaluation with Microsoft.Extensions.AI.Evaluation, cost and reliability, and prompt injection defenses. All examples run offline on .NET 10 with a clearly labelled fake chat client and embedding generator, with real output. 14 lessons including a practice set and a 25-question mock interview.
Module 1 · Foundations
3 lessons · 50 minModule 2 · Tools and Structured Output
2 lessons · 33 minModule 3 · Retrieval-Augmented Generation
3 lessons · 53 min- 06Embeddings in .NET: IEmbeddingGenerator, Cosine Similarity and Storage Math (included with Full Access)15 min
- 07RAG Ingestion and Vector Stores: Chunking, Microsoft.Extensions.VectorData, Azure AI Search and pgvector (included with Full Access)20 min
- 08RAG Answer Quality: Hybrid Retrieval, Reciprocal Rank Fusion, Grounding and Citations (included with Full Access)18 min
Module 4 · Agents, Evals and Production
4 lessons · 1 h 11 min- 09Agents with Microsoft Agent Framework: Instructions, Tools, Sessions and Agent-as-Tool (included with Full Access)18 min
- 10Evaluating LLM Features: Golden Sets, Microsoft.Extensions.AI.Evaluation and Regression Gates (included with Full Access)18 min
- 11Cost, Latency and Reliability: Token Math, Context Budgets, Retries and Fallback Models (included with Full Access)17 min
- 12Safety: Prompt Injection, Tool Approval, Output Handling and Data Protection (included with Full Access)18 min