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.

Michael T avatar

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€’πŸ’°$69 One-Time Paymentβ€’Lifetime Updates

View Roadmap

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.

Become an AI Engineer Without Leaving .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.

Michael T avatar

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€’πŸ’°$69 One-Time Paymentβ€’Lifetime Updates

View Roadmap

Used by developers in USA β€’ Australia β€’ Canada β€’ UK β€’ Japan

ChatWithPdf.Api
RAG Pipeline
PDF→Extract Text
Text→Chunk
Chunks→Embeddings
Vectors→pgvector
Query→Retrieval
Context→OpenAI
C# Source Code
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.

chat-with-pdf.local
Chat with PDF RAG application built with .NET

The included Chat with PDF application

01

Upload a PDF

The application extracts the document content and prepares it for processing.

02

Build the knowledge layer

The document is split into chunks, converted into embeddings, and stored in PostgreSQL with pgvector.

03

Retrieve relevant context

When a user asks a question, vector search finds the most relevant document chunks.

04

Generate a grounded answer

The retrieved context is sent to the LLM so the answer is grounded in the uploaded document.

AI Pipeline

PDFChunkEmbeddingpgvectorRetrieveLLM

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.

WHAT YOU'LL BUILD

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

PDF
Upload
Text
Extract
Chunks
Split
Vectors
Embed
LLM
Generate

Runtime flow

User Question β†’ Similarity Search β†’ Retrieved Context β†’ LLM β†’ Answer + Sources

RAG
01

PDF Upload

Upload a PDF and start building your knowledge base.

02

Document Processing

Process documents and prepare them for AI retrieval.

03

Text Extraction

Extract usable text from real PDF documents.

04

Chunking

Split documents into meaningful retrieval chunks.

05

Embeddings

Turn document chunks into vector representations.

06

Vector Storage

Store embeddings with PostgreSQL and pgvector.

07

Similarity Search

Find the most relevant chunks for each question.

08

RAG Retrieval

Build context from retrieved document knowledge.

09

LLM Generation

Generate answers using the retrieved context.

10

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.

SEE THE ACTUAL SOURCE CODE

Don't Just Read About AI Architecture.
Read the Code.

Follow the actual C# implementation behind the Chat with PDF RAG system.

C# / .NET

Solution Explorer

β–ΎπŸ“ChatWithPdf.Api
β–ΈπŸ“Controllers
β–ΈπŸ“Services
πŸ“„PdfExtractionService.cs
πŸ“„ChunkingService.cs
πŸ“„EmbeddingService.cs
πŸ“„RagService.cs
β–ΈπŸ“Data
β–ΈπŸ“Domain
πŸ“„Program.cs
RagService.cs
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);
    }
}

PDF

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.

WHAT'S INSIDE THE PLAYBOOK

From AI Fundamentals
to Production AI

A practical roadmap for .NET developers who want to understand how modern AI systems are actually built.

01

AI Fundamentals

Understand the core concepts behind modern AI applications.

What
Why
How
.NET Implementation
02

OpenAI with .NET

Build AI features directly inside your .NET applications.

What
Why
How
.NET Implementation
03

Embeddings

Turn text into vectors and understand semantic representation.

What
Why
How
.NET Implementation
04

Vector Search

Store and retrieve relevant knowledge using vector databases.

What
Why
How
.NET Implementation
05

RAG

Connect your application data with LLMs using retrieval-augmented generation.

What
Why
How
.NET Implementation
06

AI Agents

Build AI systems that can reason, use tools, and complete tasks.

What
Why
How
.NET Implementation
07

MCP

Connect AI models with external tools, systems, and data.

What
Why
How
.NET Implementation
08

Production AI

Move beyond demos with reliability, observability, security, and cost control.

What
Why
How
.NET Implementation

Every chapter follows the same framework

01

What

Understand the concept

02

Why

Understand why it matters

03

How

See how the system works

04

.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

$69

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.

01

LLM Fundamentals

Understand how Large Language Models work, tokens, context windows, prompting, and model limitations.

LLMsTokensPrompt Engineering
02

Embeddings & Vector Search

Learn how to convert text into embeddings, store vectors, and retrieve semantically relevant information.

EmbeddingsVector Searchpgvector
03

Retrieval-Augmented Generation

Build RAG pipelines with document processing, chunking, retrieval, context construction, and citations.

RAGChunkingRetrieval
04

AI Agents & Tool Calling

Understand how AI agents use tools, execute tasks, and interact with external systems.

AI AgentsFunction CallingMCP
05

AI Application Architecture

Design AI-powered backend applications using ASP.NET Core, services, APIs, and data storage.

ASP.NET CoreArchitectureAPIs
06

Production AI Engineering

Explore reliability, performance, cost optimization, evaluation, and production considerations.

EvaluationPerformanceCost
WHAT YOU'LL BUILD

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

PDF
Upload
Text
Extract
Chunks
Split
Vectors
Embed
LLM
Generate

Runtime flow

User Question β†’ Similarity Search β†’ Retrieved Context β†’ LLM β†’ Answer + Sources

RAG
01

PDF Upload

Upload a PDF and start building your knowledge base.

02

Document Processing

Process documents and prepare them for AI retrieval.

03

Text Extraction

Extract usable text from real PDF documents.

04

Chunking

Split documents into meaningful retrieval chunks.

05

Embeddings

Turn document chunks into vector representations.

06

Vector Storage

Store embeddings with PostgreSQL and pgvector.

07

Similarity Search

Find the most relevant chunks for each question.

08

RAG Retrieval

Build context from retrieved document knowledge.

09

LLM Generation

Generate answers using the retrieved context.

10

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

The Problem

Learning AI today is overwhelming.

Most .NET developers waste months jumping between tutorials, switching technologies, and trying to figure out what actually matters.

❌ Random Tutorials

Endless YouTube videos with no clear learning path.

❌ Python Everywhere

Most AI courses assume you're a Python developerβ€”not a .NET engineer.

❌ No Enterprise Architecture

Tutorials rarely explain how AI systems are designed for production.

❌ Toy Projects

Building chatbots is easy. Building real AI applications is different.

❌ No Clear Roadmap

Developers don't know what to learn first or what to skip.

❌ 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 developersβ€”covering everything from AI fundamentals to production-ready AI applications.

The Solution

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.

πŸ“„

Retrieval-Augmented Generation

Learn how to build RAG applications using embeddings, vector databases, and semantic search.

πŸ€–

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.

πŸ”Œ

Model Context Protocol

Connect AI models with external tools, databases, APIs, and enterprise services using MCP.

πŸš€

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 β†’
Hands-on Projects

Build Real AI Applications

Learn by building complete AI systems using C#, ASP.NET Core, OpenAI, Semantic Kernel, Vector Databases, and Azure AI.

πŸ“„

Chat with PDF

Build your first Retrieval-Augmented Generation application.

Upload PDF
↓
Extract Text
↓
Chunking
↓
Generate Embeddings
↓
Store in Qdrant
↓
Similarity Search
↓
GPT Response
↓
Answer User
πŸ€–

AI Agent

Build an autonomous AI agent with tools and memory.

User Request
↓
LLM Planning
↓
Tool Calling
↓
Database/API
↓
Reasoning
↓
Memory
↓
Final Response

And many more projects...

πŸ’¬ AI Chatbot
πŸ”Ž Semantic Search
🧠 AI Memory
πŸ“š Vector Database
☁ Azure OpenAI
⚑ Semantic Kernel
πŸ”Œ MCP Server
πŸš€ Production Deployment
FROM .NET DEVELOPER β†’ AI ENGINEER

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.

C#

Your foundation

.NET Engineering

C#
ASP.NET Core
SQL
EF Core
APIs
Dependency Injection
+

AI

AI

New capabilities

AI Engineering

LLMs
Embeddings
RAG
Vector Search
Agents
MCP
Your existing engineering foundation + AI capabilities

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.

.NET→APIs→Data→AI→RAG→Agents→AI Applications
WHAT YOU GET

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

01
AI Engineering Playbook
βœ“
02
Chat with PDF Source Code
βœ“
03
RAG implementation
βœ“
04
Architecture diagrams
βœ“
05
Implementation guides
βœ“
06
AI Engineering roadmap
βœ“
07
Lifetime updates
βœ“
ONE-TIME PURCHASE

AI Engineering with .NET

$69one-time

Get the complete playbook and the Chat with PDF source code. Lifetime updates included.

One payment Β· Lifetime access Β· Lifetime updates

PlaybookIncluded
Source CodeIncluded
UpdatesLifetime

One payment. Build, study, modify, and keep learning.

No subscription. No recurring payment.

WHO THIS IS FOR

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.

β†’

For

Who this is built for

βœ“
.NET developers
βœ“
ASP.NET Core developers
βœ“
Backend engineers
βœ“
Software engineers learning AI
βœ“
Developers building AI features
>_

Prerequisites

You should already know

βœ“
Basic C#
βœ“
Basic .NET
βœ“
REST APIs
βœ“
Basic SQL

No advanced AI experience is required. The playbook takes you from the fundamentals to building real AI applications with .NET.

If you can build a .NET API, you have the foundation.

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

About the Author

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.

Author
Testimonials

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."

D

David Chen

Senior .NET Developer

⭐⭐⭐⭐⭐

"The RAG, Semantic Kernel, and AI Agent chapters alone are worth the price. This saved me weeks of research."

M

Michael Johnson

Software Architect

⭐⭐⭐⭐⭐

"I loved that it focuses on production architecture instead of toy examples. Exactly what I was looking for."

S

Sarah Williams

.NET Backend Engineer

Lifetime Access

Everything Included

One payment. Lifetime access. Free future updates.

βœ“60+ Lessons
βœ“Real AI Projects
βœ“Skills
βœ“Architecture Diagrams
βœ“Lifetime Updates
βœ“Chat with PDF Source Code
βœ“Future Chapters Included
βœ“Beginner β†’ Senior Roadmap
$149
$69

Pay once. Learn forever.

βœ” Instant Access Β  β€’ Β  βœ” Lifetime Updates Β  β€’ Β  βœ” Secure Checkout

FAQ

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.

πŸš€ Start Your AI Journey Today

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.

βœ…Lifetime Access
⚑Instant Download
πŸ”„Free Future Updates
πŸ’³Secure Checkout