Become an AI Engineer with .NET
From .NET Developer to AI Engineer.
Turn your .NET skills into AI Engineering opportunities. Learn to build real AI applications with C# and .NET, from LLMs and RAG to AI agents.
Stop guessing how AI works. Learn to build production-ready AI applications with C# and .NET
β from your first LLM integration to complete RAG systems and AI agents.

From Calling AI APIs to Understanding AI Systems
"I knew how to integrate AI APIs, but not how AI systems worked behind the scenes. This playbook helped me understand and build real AI applications with .NET."
Michael T. Β· .NET Developer @ US Company
Trusted by 1,500+ Developersβ’π°$69 One-Time Paymentβ’Lifetime Updates
Used by developers in USA β’ Australia β’ Canada β’ UK β’ Japan
Save Learning Time
Follow a structured learning path instead of spending months figuring everything out yourself.
Grow Your Income Potential
Build in-demand AI engineering skills and unlock higher-value career opportunities.
From .NET Developer to AI Engineer.
Turn your .NET skills into AI Engineering opportunities. Learn to build real AI applications with C# and .NET, from LLMs and RAG to AI agents.

From Calling AI APIs to Understanding AI Systems
"I knew how to integrate AI APIs, but not how AI systems worked behind the scenes. This playbook helped me understand and build real AI applications with .NET."
Michael T. Β· .NET Developer @ US Company
Trusted by 1,500+ Developersβ’π°$69 One-Time Paymentβ’Lifetime Updates
Used by developers in USA β’ Australia β’ Canada β’ UK β’ Japan
public async Task<Answer> AskAsync(
string question,
CancellationToken ct)
{
var embedding =
await _embeddings
.CreateAsync(question);
var chunks =
await _vectorStore
.SearchAsync(embedding);
return await _llm
.GenerateAsync(chunks);
}Build a Real AI Application
Don't Just Learn RAG.Build It.
Learn how a real AI application processes documents, retrieves relevant information, and generates grounded answers using .NET.

The included Chat with PDF application
Upload a PDF
The application extracts the document content and prepares it for processing.
Build the knowledge layer
The document is split into chunks, converted into embeddings, and stored in PostgreSQL with pgvector.
Retrieve relevant context
When a user asks a question, vector search finds the most relevant document chunks.
Generate a grounded answer
The retrieved context is sent to the LLM so the answer is grounded in the uploaded document.
AI Pipeline
This is RAG in practice.
You don't just get a diagram explaining RAG. You get a real .NET application that you can read, run, debug, and extend.
Build a Complete Chat with PDF RAG System
Don't build another toy AI demo.
Build one complete application and understand how every layer works.
AI APPLICATION PIPELINE
PDF β RAG β LLM
Runtime flow
User Question β Similarity Search β Retrieved Context β LLM β Answer + Sources
PDF Upload
Upload a PDF and start building your knowledge base.
Document Processing
Process documents and prepare them for AI retrieval.
Text Extraction
Extract usable text from real PDF documents.
Chunking
Split documents into meaningful retrieval chunks.
Embeddings
Turn document chunks into vector representations.
Vector Storage
Store embeddings with PostgreSQL and pgvector.
Similarity Search
Find the most relevant chunks for each question.
RAG Retrieval
Build context from retrieved document knowledge.
LLM Generation
Generate answers using the retrieved context.
Source Citations
Show users exactly where each answer comes from.
One real application.
Ten layers of AI engineering knowledge.
You won't just learn what RAG is. You'll understand how the entire system works from document ingestion to the final answer.
Don't Just Read About AI Architecture.
Read the Code.
Follow the actual C# implementation behind the Chat with PDF RAG system.
Solution Explorer
public class RagService
{
private readonly IEmbeddingService _embedding;
private readonly AppDbContext _db;
public RagService(
IEmbeddingService embedding,
AppDbContext db)
{
_embedding = embedding;
_db = db;
}
public async Task<RagResult> AskAsync(
string question)
{
// 1. Embed the question
var vector =
await _embedding.CreateAsync(question);
// 2. Retrieve relevant chunks
var chunks =
await _db.DocumentChunks
.OrderBy(c => c.Embedding
.CosineDistance(vector))
.Take(5)
.ToListAsync();
// 3. Build context for the LLM
var context =
BuildContext(chunks);
// 4. Generate answer + citations
return await GenerateAnswerAsync(
question,
context);
}
}Extraction
Chunks
Embeddings
pgvector
Retrieval
LLM
Generation
The complete C# source code is included.
Read the implementation, trace the data flow, modify the architecture, and use the project as a foundation for your own AI applications.
From AI Fundamentals
to Production AI
A practical roadmap for .NET developers who want to understand how modern AI systems are actually built.
AI Fundamentals
Chapter 01Understand the core concepts behind modern AI applications.
OpenAI with .NET
Chapter 02Build AI features directly inside your .NET applications.
Embeddings
Chapter 03Turn text into vectors and understand semantic representation.
Vector Search
Chapter 04Store and retrieve relevant knowledge using vector databases.
RAG
Chapter 05Connect your application data with LLMs using retrieval-augmented generation.
AI Agents
Chapter 06Build AI systems that can reason, use tools, and complete tasks.
MCP
Chapter 07Connect AI models with external tools, systems, and data.
Production AI
Chapter 08Move beyond demos with reliability, observability, security, and cost control.
Every chapter follows the same framework
What
Understand the concept
Why
Understand why it matters
How
See how the system works
.NET
Implement it in C#
Learn the concept.
Then build it with .NET.
Build AI with the skills
you already have.
AI Engineering with .NET
Chat with PDF Source Code
Why Learn AI Engineering?
Make Your Learning More Valuable.
Don't spend months figuring everything out on your own. Focus on the skills that help you build real AI applications and create new career opportunities.
Save Learning Time
Stop jumping between random tutorials and disconnected resources. Follow a structured learning path that takes you from AI fundamentals to building complete applications with C# and .NET.
Learn what matters. Follow a clear path.
Grow Your Income Potential
Expand beyond traditional .NET development. Build practical AI engineering skills that help you qualify for more valuable projects and explore higher-paying career opportunities.
Turn your .NET experience into new opportunities.
What You Will Learn
Master the Core Concepts of AI Engineering.
Go beyond calling AI APIs. Understand the technologies behind modern AI applications and learn how to use them with C# and .NET.
LLM Fundamentals
Understand how Large Language Models work, tokens, context windows, prompting, and model limitations.
Embeddings & Vector Search
Learn how to convert text into embeddings, store vectors, and retrieve semantically relevant information.
Retrieval-Augmented Generation
Build RAG pipelines with document processing, chunking, retrieval, context construction, and citations.
AI Agents & Tool Calling
Understand how AI agents use tools, execute tasks, and interact with external systems.
AI Application Architecture
Design AI-powered backend applications using ASP.NET Core, services, APIs, and data storage.
Production AI Engineering
Explore reliability, performance, cost optimization, evaluation, and production considerations.
Build a Complete Chat with PDF RAG System
Don't build another toy AI demo.
Build one complete application and understand how every layer works.
AI APPLICATION PIPELINE
PDF β RAG β LLM
Runtime flow
User Question β Similarity Search β Retrieved Context β LLM β Answer + Sources
PDF Upload
Upload a PDF and start building your knowledge base.
Document Processing
Process documents and prepare them for AI retrieval.
Text Extraction
Extract usable text from real PDF documents.
Chunking
Split documents into meaningful retrieval chunks.
Embeddings
Turn document chunks into vector representations.
Vector Storage
Store embeddings with PostgreSQL and pgvector.
Similarity Search
Find the most relevant chunks for each question.
RAG Retrieval
Build context from retrieved document knowledge.
LLM Generation
Generate answers using the retrieved context.
Source Citations
Show users exactly where each answer comes from.
One real application.
Ten layers of AI engineering knowledge.
You won't just learn what RAG is. You'll understand how the entire system works from document ingestion to the final answer.
Your Complete AI Engineering Toolkit
What's Included in the Playbook?
Everything you need to understand AI engineering concepts and explore real implementations with .NET.
AI Engineering Playbook
A structured learning resource covering LLMs, embeddings, vector search, RAG, AI agents, and AI application architecture.
Complete Chat with PDF RAG Source Code
Explore a practical implementation using ASP.NET Core, OpenAI, embeddings, PostgreSQL, and pgvector.
Architecture Diagrams
Understand how the components of modern AI applications work together through visual architecture diagrams.
Practical Implementation Guides
Follow practical explanations and implementation steps to understand how AI-powered applications are built.
Lifetime Updates
Get future updates to the playbook and included learning materials without recurring payments.
$69
One-time payment Β· Lifetime updates
Learning AI today is overwhelming.
Most .NET developers waste months jumping between tutorials, switching technologies, and trying to figure out what actually matters.
β Random Tutorials
Endless YouTube videos with no clear learning path.
β Python Everywhere
Most AI courses assume you're a Python developerβnot a .NET engineer.
β No Enterprise Architecture
Tutorials rarely explain how AI systems are designed for production.
β Toy Projects
Building chatbots is easy. Building real AI applications is different.
β No Clear Roadmap
Developers don't know what to learn first or what to skip.
β Too Much AI Hype
It's hard to separate practical engineering skills from marketing buzz.
That's exactly why this playbook exists.
Instead of learning from dozens of disconnected resources, you'll follow a structured roadmap designed specifically for .NET developersβcovering everything from AI fundamentals to production-ready AI applications.
A structured learning path
designed specifically for .NET developers
Follow a step-by-step roadmap that takes you from AI fundamentals to building production-ready AI applications using the Microsoft ecosystem.
AI Fundamentals
Understand LLMs, prompts, embeddings, tokens, vector search, and the core concepts behind modern AI systems.
Retrieval-Augmented Generation
Learn how to build RAG applications using embeddings, vector databases, and semantic search.
AI Agents
Build intelligent agents capable of reasoning, tool calling, planning, and multi-step workflows.
Semantic Kernel
Master Microsoft's official AI SDK for orchestrating prompts, plugins, memory, and agents.
Model Context Protocol
Connect AI models with external tools, databases, APIs, and enterprise services using MCP.
Production Deployment
Deploy scalable AI applications with Docker, Azure AI, monitoring, caching, and best practices.
Everything you need to become an AI Engineer with .NET
No Python required. No random tutorials. Just a complete, practical roadmap focused on building real-world AI applications with C# and the Microsoft AI ecosystem.
Get Lifetime Access βBuild Real AI Applications
Learn by building complete AI systems using C#, ASP.NET Core, OpenAI, Semantic Kernel, Vector Databases, and Azure AI.
Chat with PDF
Build your first Retrieval-Augmented Generation application.
AI Agent
Build an autonomous AI agent with tools and memory.
And many more projects...
You Don't Have to Leave .NET
to Build AI.
Your existing engineering skills are not something you need to replace.
They are the foundation for building AI applications.
Your foundation
.NET Engineering
AI
New capabilities
AI Engineering
You already know how to build software.
Now add AI to your engineering toolbox.
Keep using C#, ASP.NET Core, APIs, databases, dependency injection, and the architecture principles you already know. Learn how LLMs, RAG, agents, and MCP fit into that world.
Everything You Need to
Start Building AI with .NET
A practical playbook, a real application, and the source code behind it.
Included
Everything inside the package
AI Engineering with .NET
Get the complete playbook and the Chat with PDF source code. Lifetime updates included.
One payment Β· Lifetime access Β· Lifetime updates
One payment. Build, study, modify, and keep learning.
No subscription. No recurring payment.
Built Specifically for
.NET Developers Moving into AI
You don't need to become a different kind of developer.
You need to add AI engineering skills to the foundation you already have.
For
Who this is built for
Prerequisites
You should already know
No advanced AI experience is required. The playbook takes you from the fundamentals to building real AI applications with .NET.
Learning Roadmap
Module 1
AI Fundamentals
Module 2
Large Language Models (LLMs)
Module 3
Prompt Engineering
Module 4
Embeddings & Vector Databases
Module 5
Retrieval-Augmented Generation (RAG)
Module 6
Semantic Kernel
Module 7
Microsoft Agent Framework
Module 8
Model Context Protocol (MCP)
Module 9
AI Agents & Multi-Agent Systems
Module 10
Deploying AI Applications to Azure
Build Real AI Projects
AI Chat Assistant
PDF Chat with RAG
SQL AI Agent
Customer Support Agent
AI Code Reviewer
Multi-Agent Workflow
Hi, I'm Cuong.
I'm a Senior .NET Engineer passionate about helping developers build modern, production-ready AI applications using the Microsoft ecosystem.
After spending years building enterprise software, I realized there wasn't a structured AI roadmap designed specifically for .NET developers.
This playbook contains everything I wish I had when I started learning AI Engineering.
Loved by .NET Developers
Developers are using this roadmap to transition into AI Engineering without leaving the Microsoft ecosystem.
"Finally, an AI playbook built specifically for .NET developers. Everything is structured and practical instead of random tutorials."
David Chen
Senior .NET Developer
"The RAG, Semantic Kernel, and AI Agent chapters alone are worth the price. This saved me weeks of research."
Michael Johnson
Software Architect
"I loved that it focuses on production architecture instead of toy examples. Exactly what I was looking for."
Sarah Williams
.NET Backend Engineer
Everything Included
One payment. Lifetime access. Free future updates.
Pay once. Learn forever.
β Instant Access Β β’ Β β Lifetime Updates Β β’ Β β Secure Checkout
Frequently Asked Questions
Everything you need to know before getting started.
Yes. The roadmap starts with AI fundamentals before moving into advanced topics like RAG, AI Agents, Semantic Kernel, and production deployment.
Ready to Build
AI Applications with .NET?
Join hundreds of .NET developers learning AI the practical way. Stop watching random tutorials and start building production-ready AI applications with C#, ASP.NET Core, Semantic Kernel, AI Agents, RAG, Azure AI, and more.