Build AI-Powered Applications in Modern C++

Build cross platform AI applications in Modern C++ with OpenAI, Ollama, APIs, JSON, and real projects
What you'll learn

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Build five complete AI-powered C++ applications from scratch
Understand how AI APIs work at the HTTP and JSON level, not just how to call them
Connect C++ directly to OpenAI's GPT models and run AI locally for free using Ollama
Gain skills transferable to real-world AI application development in any language
Add demonstrable AI integration projects to your portfolio
Develop the confidence to integrate new AI services independently after the course ends
Build a solid understanding of JSON handling and HTTP/HTTPS communication in C++ using industry-standard libraries
Construct AI API requests, parse structured responses, and handle errors correctly
Learn the Builder Pattern, dependency injection, and composition through real projects
Build a complete Retrieval-Augmented Generation pipeline from scratch in C++
Design applications that support multiple AI backends with minimal code changes
Understand what AI wrapper libraries are doing underneath
Build a foundation that outlasts changing APIs, models, and tools
Develop architectural thinking that applies well beyond this course
Requirements

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Intermediate C++ knowledge: variables, functions, loops, conditionals, and basic classes
Beginner-level familiarity with pointers and references
Comfortable with Windows 10/11, macOS (current or up to 2 versions behind), or Ubuntu 24.04 LTS
No Python knowledge required
Able to use a terminal, install software, and manage files
Machine capable of running CLion IDE and Docker Desktop
8GB RAM minimum, 16GB recommended
15-20GB free disk space
Reliable internet connection
Some exposure to object-oriented design
Familiarity with any IDE
No prior AI or machine learning experience
No prior AI or machine learning experience
No GPU required
Description

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Build AI-Powered Applications in Modern C++
Ready to take your C++ skills into the world of Artificial Intelligence?
In this hands-on course, you'll learn how to buildreal AI-powered applications in modern C++ by working directly with AI APIs, HTTP/HTTPS communication, JSON, embeddings, vector representations, and Retrieval-Augmented Generation (RAG).
Instead of hiding the complexity behind pre-built AI SDKs or wrapper libraries, this course takes aninternals-first approach. You'll see exactly how requests are constructed, how JSON data is exchanged, how responses are parsed, and how AI services can be integrated into a C++ application.
You'll also learn how to connect C++ applications toOpenAI GPT models and run AI models locally usingOllama, giving you the flexibility to work with cloud-based or local AI.
What You'll Build
Throughout the course, you'll progressively buildfive complete AI-powered C++ projects, with each project introducing new concepts and extending the architecture of the previous one.
You'll learn how to

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Build AI applications using C++ and the OpenAI API
Run AI models locally with Ollama
Send and process HTTP/HTTPS requests directly from C++
Work with JSON using the industry-standard nlohmann-json library
Build and parse structured AI responses
Create embeddings and perform similarity searches
Build a completeRetrieval-Augmented Generation (RAG) system from scratch in C++
Apply the Builder Pattern with method chaining
Progress from simple procedural code to well-structured object-oriented applications
Apply dependency injection and composition in real projects
Design applications that can support multiple AI backends with minimal code changes
Why This Course Is Different
Most AI application development courses rely heavily on Python, wrapper libraries, or pre-built SDKs.
This course takes a different approach.
You'll build acomplete RAG pipeline in C++, giving you a deeper understanding of how modern AI applications actually work while demonstrating that C++ is a powerful choice for AI application development.
You'll also build the same chatbot progressively - from a monolithic implementation, to functions, and finally to a full object-oriented design. This allows you to seewhy architectural improvements matter and when to apply them.
All five projects support bothOpenAI and Ollama, allowing you to switch between cloud-based and local AI with minimal configuration changes.
By the end of the course, you won't simply know how to call an AI API. You'll understand the underlying technologies well enough tointegrate new AI services independently as APIs, models, and tools continue to evolve.
What You'll Need
You should have intermediate C++ knowledge, including variables, functions, loops, conditionals, and basic classes. Beginner-level familiarity with pointers and references is also recommended.
You'll need a computer capable of running CLion and Docker Desktop, withat least 8 GB of RAM (16 GB recommended) and approximately15-20 GB of available disk space.
No previous AI or machine learning experience is required.
No Python knowledge is required.
No GPU is required.
By the End of This Course
You'll have practical experience building AI-powered applications in C++ and a portfolio of projects demonstrating your ability to integrate modern AI technologies into real software.
More importantly, you'll develop the architectural understanding and confidence to go beyond this course and build your own AI-powered C++ applications.
If you're ready to combine the power of C++ with modern AI, this course will give you the practical skills and foundation to get started.
Who this course is for

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C++ developers who want to learn how to build modern AI-powered applications.
Intermediate C++ programmers who want to apply their existing C++ skills to AI application development.
Software developers interested in integrating AI services directly into their applications.
Developers who want to understand AI APIs at the HTTP and JSON level, rather than relying entirely on wrapper libraries or pre-built SDKs.
C++ developers interested in OpenAI and local AI using Ollama.
Developers who want to learn Retrieval-Augmented Generation (RAG) using C++, rather than relying exclusively on Python.
Developers who want practical, portfolio-ready AI projects they can demonstrate to employers or clients.
Programmers who want to strengthen their software architecture and object-oriented design skills through real AI projects.
Homepage

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