Udemy — Build AI-Powered Applications in Modern C++ 2026-8

Description

Integrate language-model services into modern C++ applications by working with requests, responses and data structures directly. The course develops five projects using HTTP/HTTPS communication and JSON handling with nlohmann-json. Examples connect to hosted GPT services and local models through Ollama, then extend into embeddings, similarity search and a retrieval-augmented generation pipeline.

The application architecture evolves from a single procedural implementation into functions and then an object-oriented design. Builder-style construction, method chaining, dependency injection and composition illustrate how to separate responsibilities and support different AI backends. Request construction, structured response parsing and error handling remain visible throughout, helping learners understand the integration work that a higher-level wrapper would otherwise conceal.

Intended audience: Intermediate C++ developers adding AI features to applications. Programmers interested in direct API integration, retrieval systems and application architecture.

What you will learn

• Construct HTTP/HTTPS requests and parse JSON responses in C++.
• Connect application code to hosted AI APIs and local Ollama models.
• Implement embeddings, similarity search and a C++ retrieval pipeline.
• Refactor an application using classes, composition, dependency injection and a Builder pattern.
• Design an integration that can switch between AI backends.

Course specifications

Publisher

Udemy

Instructors

Tim Buchalka’s Learn Programming Academy, Wyzdom AI Inc, Dr. Frank Mitropoulos

Language

English

Level

Intermediate

Duration

12 hours and 29 minutes

Subtitle

Not Available

Quality

1080p

Edition

2026-8

Course topics

View course topics

Prerequisites

• Intermediate C++, including functions, control flow and basic classes, plus introductory pointers and references.
• Comfort using a terminal, installing software and working in an IDE.
• A computer capable of running CLion and Docker Desktop; the source lists 8 GB RAM minimum, 16 GB recommended and 15–20 GB of available storage.
• The listed environments are Windows 10/11, recent macOS or Ubuntu 24.04 LTS.
• No prior Python, AI or machine-learning study is required; a GPU is not listed as necessary.

Introduction video

Download resources

Choose one edition and keep its parts together. Alternate providers are available when listed.

Size

7.25 GB

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