AI Agents with Modern C++: RAG, Tools & Agentic AI

About this course

AI Engineering with Modern C++: LLMs, RAG, AI Agents & GenAILearn 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 into C++ applications, combining the performance and flexibility of C++ with today's powerful AI technologies.

This is a hands-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.

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