LLM Engineering Masterclass: RAG & AI Applications

About this course

This course contains the use of artificial intelligence.

Are you ready to move beyond simply calling Large Language Models (LLMs) and start building production-ready AI applications used in modern organizations?

Welcome to LLM Engineering Masterclass: RAG & AI Applications, a comprehensive hands-on course designed to teach the practical skills required to become an AI Engineer. Instead of focusing only on prompt engineering or basic API calls, this course takes you through the complete lifecycle of designing, building, optimizing, securing, and deploying enterprise-grade LLM applications using Python.

You will begin by understanding the role of an AI Engineer, the evolution from traditional software development, and why Retrieval-Augmented Generation (RAG) has become the industry standard for building reliable AI systems. You will learn how to work directly with LLM APIs, manage tokens, optimize context windows, reduce API costs, and implement streaming responses for real-world applications.

As the course progresses, you will explore embeddings and vector mathematics before working with modern vector databases including Pinecone, Qdrant, and ChromaDB. You will learn professional data ingestion techniques, document chunking strategies, metadata enrichment, and semantic search to build highly accurate retrieval systems.

The course then guides you through building complete RAG pipelines from scratch using native Python, implementing conversational memory, hybrid search, metadata filtering, and advanced re-ranking techniques to improve response quality.

You will also explore Agentic AI concepts including intelligent routing, reflection loops, and multi-step reasoning workflows that enable more capable AI systems.

Finally, you will focus on production engineering by securing LLM applications against prompt injection attacks, managing API secrets, containerizing applications with Docker, deploying scalable REST APIs, monitoring LLM perfo

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