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AI Engineering with Modern C++ LLMs, RAG, AI Agents & GenAI
Published 9/2026
Created by Sanjeevan DSouza
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 70 Lectures ( 13h 48m ) | Size: 9.8 GB
Learn LLM Integration, Retrieval-Augmented Generation, and Agentic AI Using Modern C++
What you'll learn
Build AI applications in C++ using OpenAI APIs, embeddings, and large language models. Create Vector RAG systems that retrieve and generate accurate responses from custom knowledge bases Implement Hybrid RAG pipelines combining vector search and keyword search for improved retrieval quality. Enhance RAG performance with reranking models to improve relevance and response accuracy. Develop Agentic RAG systems that use tools, reasoning, and retrieval to solve complex tasks autonomously.Requirements
Basic knowledge of C++ programming, including functions, classes, pointers, and STL containers. Familiarity with object-oriented programming concepts such as encapsulation, inheritance, and polymorphism. A Windows, Linux, or macOS computer capable of installing development tools and running C++ applications. A C++ compiler and development environment such as Visual Studio, GCC, or Clang. Basic understanding of command-line operations for building and running applications. An internet connection to access AI APIs, download libraries, and course resources. No prior AI, Machine Learning, or Data Science experience is required. No prior knowledge of RAG, vector databases, embeddings, reranking, or AI agents is required.Description
AI Engineering with Modern C++: LLMs, RAG, AI Agents & GenAI
Learn how to build real-world AI applications with Modern C++. Work with Large Language Models (LLMs), Generative AI, embeddings, vector search, Retrieval-Augmented Generation (RAG), AI reranking, local LLMs, and AI agents.
Most Generative AI courses focus on Python. This course takes a different approach: you will learn how to integrate modern AI capabilities directly intoC++ applications, combining the performance and flexibility of C++ with today's powerful AI technologies.
This is ahands-on AI Engineering course focused on building working applications rather than studying AI theory. You will progressively move from your first LLM API call to advanced RAG pipelines, hybrid retrieval, reranking, AI agents, and Agentic RAG.
What You Will Learn
By the end of this course, you will be able to
Build AI-powered applications using Modern C++ and LLM APIs. Integrate OpenAI and Groq APIs into C++ applications. Understand tokens, embeddings, tensors, context windows, and LLM inference. Generate embeddings and implement semantic search. Build searchable knowledge bases using document chunking and embeddings. Implement Retrieval-Augmented Generation (RAG) applications. Build Vector RAG using SQLite and FAISS. Combine keyword and semantic search using BM25 and vector search. Improve retrieval quality using Reciprocal Rank Fusion (RRF). Apply AI reranking models to improve RAG results. Run local LLMs using llama.cpp and GGUF models. Build AI agents that use tools and perform multi-step tasks. Design orchestrated AI agent workflows. Build Agentic RAG applications that combine retrieval, reasoning, and tools.What You Will Build
Throughout the course, you will build practical C++ AI applications including
LLM applications using OpenAI and Groq APIs Local LLM applications using llama.cpp Semantic search engines using embeddings Searchable knowledge bases Vector RAG applications using SQLite High-performance Vector RAG using FAISS Hybrid RAG using BM25, vector search, and RRF RAG systems enhanced with AI reranking Calculator and orchestrated AI agents An Agentic RAG-based AI medical assistantCourse Roadmap
The course follows a progressive path from AI fundamentals to advanced AI Engineering
AI Engineering Fundamentals → LLM APIs → Local LLMs → Embeddings → Vector Search → RAG → FAISS → Hybrid RAG → Reranking → AI Agents → Agentic RAG
You will build upon each concept as you progress, giving you a practical understanding of how modern AI applications are designed and implemented.
Technologies You Will Use
Modern C++ • OpenAI API • Groq API • llama.cpp • SQLite • FAISS • BM25 • Reciprocal Rank Fusion • ONNX Runtime • Embeddings • Vector Search • RAG • AI Agents
Why Learn AI Engineering with C++?
C++ remains essential for high-performance software, systems programming, enterprise applications, edge computing, and performance-sensitive workloads.
This course shows you how to bring modernGenerative AI and LLM capabilities into the C++ ecosystem. Instead of learning AI only through Python notebooks, you will learn how AI technologies can be integrated into real C++ applications and systems.
Who Is This Course For?
This course is designed for
C++ developers who want to move into AI Engineering. Software engineers interested in Generative AI and LLM applications. Backend and systems developers building AI-powered software. Developers who want to learn RAG and semantic search. Programmers interested in AI agents and Agentic AI. Developers who want to run LLMs locally with C++. Students and professionals looking for practical AI Engineering skills.Prerequisites
Basic C++ programming knowledge is recommended.
You do not need prior experience with LLMs, RAG, embeddings, vector databases, AI agents, or Generative AI. These concepts are introduced progressively throughout the course.
Start Building AI with Modern C++
If you are a C++ developer looking to move beyond traditional software development and start building modern AI applications, this course gives you a practical path fromLLM fundamentals to RAG, vector search, reranking, AI agents, and
Agentic RAG.
Learn the concepts, write the C++ code, build the applications, and develop the skills needed to engineer modern AI systems with C++.
Who this course is for
C++ developers who want to learn how to build AI-powered applications using modern AI engineering techniques. Software engineers interested in integrating Large Language Models (LLMs) into their C++ applications. Developers who want to understand and implement Retrieval-Augmented Generation (RAG) systems from scratch. Programmers looking to build intelligent AI assistants, chatbots, and agent-based applications. Backend and systems programmers who want to add AI capabilities to enterprise and production software. Students and technology enthusiasts interested in practical AI engineering rather than AI theory or research. Developers who want hands-on experience with OpenAI APIs, embeddings, vector search, reranking, and Agentic RAG. C++ professionals seeking to expand their skills in Generative AI and stay current with modern AI application development.Homepage
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