Build AI systems that
The only course that goes from token to deploy: LLMs, RAG, Agents, Harness Engineering, MCP and observability, all with real code in .NET.
Not another prompt course. This is engineering.
Every module ends with code you can ship on Monday. No endless slides, no theory disconnected from practice.
Harness Engineering as a discipline
The model is 20% of the product. The other 80% is the execution loop, tool routing, context management, permissions and telemetry. You will build a complete harness in C#, from executor to sandbox, and understand why it separates demos from products.
RAG that doesn't hallucinate
Semantic chunking, hybrid search, re-ranking and faithfulness metrics.
Agents with tool use
From the basic loop to multi-agent patterns with Microsoft.Extensions.AI.
Native MCP
Model Context Protocol servers and clients with the official .NET SDK.
Full observability
Every call traced with OpenTelemetry: cost, latency and replay.
100% .NET 10 code
Real, testable, versioned examples. No pseudo-code.
8 modules. 28 lessons. Zero filler.
From fundamentals to deploy, in dependency order. Each module unlocks the next.
AI & LLM Fundamentals
How a language model really works, no magic involved.
- Tokens, embeddings and vector space
- Attention and the Transformer architecture
- Decoding: temperature, top-p and sampling
- Limits, hallucinations and how to reason about them
Production Prompt Engineering
Prompts that work at scale, not just in the demo.
- Anatomy of a system prompt
- Structured reasoning and typed outputs
- Evaluating and versioning prompts
RAG: Retrieval-Augmented Generation
Connect the model to your data with surgical precision.
- Why RAG and not fine-tuning
- Ingestion pipeline: chunking and embeddings
- Hybrid retrieval and re-ranking
- Advanced RAG: query rewriting, GraphRAG and agentic RAG
Agents & Tool Use
From passive chat to autonomous task execution.
- What an agent is (and is not)
- Tool calling with Microsoft.Extensions.AI
- Multi-agent patterns
Harness Engineering
The infrastructure that makes agents reliable in production.
- What a harness is and why the model is not enough
- Building the executor in C#
- Context management: compaction, memory and pruning
- Permissions, sandboxing and human-in-the-loop
- Agent evals and improvement loops
MCP: Model Context Protocol
The open standard for connecting models to any system.
- Introduction to MCP
- Building an MCP server in .NET
- Consuming MCP servers and security
Evals, Observability & Security
Measure, trace and protect AI systems.
- LLM-as-judge and automatic metrics
- Observability with OpenTelemetry
- Prompt injection and defense in depth
Production with .NET 10
Architecture, cost and scale for real AI apps.
- Reference architecture
- Streaming, SSE and latency UX
- Cost, caching and scale
Engineers who stopped building demos and started shipping.
The Harness module changed how I think about agents. Within two weeks I rewrote our company's executor with budgets, sandboxing and evals.
Our RAG was at 60% precision. After hybrid search and re-ranking it passed 90. The course pays for itself in a single sprint.
Finally AI content that speaks the language of .NET developers. MCP with the official SDK, OpenTelemetry, everything I use day to day.
One subscription. Everything included.
Cancel anytime. 14-day money-back guarantee, no questions asked.
The complete program: from LLM fundamentals to production-grade harnesses.
- All 8 modules and 28 lessons
- Full source code for every example
- Final project: an agent in production
- Private student community
- Weekly live office hours
- New content and updates while you're subscribed
- Verifiable certificate on completion
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Everything you need to know.
Do I need to know .NET to take the course?
What is "Harness Engineering"?
Which AI providers are used?
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The next generation of software is built with AI.
Be on the side that builds it.
Subscribe today and start the fundamentals module in under 2 minutes.
Subscribe for $19.90/month