Master LLM Engineering and AI Agents Projects Course
The Master LLM Engineering and AI Agents Projects Course is a comprehensive, hands-on program designed to equip learners with the practical skills, frameworks, and project experience required to build advanced, production-ready AI systems. From foundational LLM engineering principles to building fully functional autonomous agents, this course guides you step-by-step through real-world applications used in modern AI-driven organizations.
You will learn how to design intelligent systems that reason, plan, retrieve information, and execute actions—powered by cutting-edge language models and agentic architectures. Through structured lessons and applied projects, you’ll gain the confidence to engineer scalable AI workflows and deploy solutions that solve complex business problems.
What You’ll Learn
Core principles of LLM engineering, including prompting strategies, model evaluation, and output optimization.
How to build retrieval-augmented generation (RAG) pipelines for accurate, factual AI responses.
Techniques for designing multi-step AI agents capable of planning, tool use, function calling, and autonomous task execution.
Best practices for integrating external APIs, databases, and enterprise systems into agent workflows.
Frameworks for evaluating reliability, safety, and performance in production-grade AI systems.
How to architect and deploy end-to-end AI solutions using modern development tools and libraries.
Who This Course Is For
Developers and engineers aiming to specialize in applied AI systems.
Data scientists and analysts looking to expand into LLM-powered automation.
Technical professionals seeking hands-on, project-based experience with AI agents.
Anyone who wants to learn how to build real, deployable AI applications—not just theory.
Course Outcomes
By the end of the course, you will be able to:
Build and deploy intelligent applications using LLMs, RAG, tools, and agents.
Develop AI agents that autonomously research, solve problems, and interact with digital environments.
Apply industry best practices for testing, safety, optimization, and monitoring.
Create a portfolio of practical AI engineering projects demonstrating your mastery.