AI Engineering Fundamentals
- 1 Video Lesson
Learn the core concepts every AI Engineer needs—from Generative AI and Large Language Models (LLMs) to Prompt Engineering, RAG, MCP, Embeddings, Semantic Search, Structured Outputs, and AI Agents. This beginner-friendly course focuses on practical understanding rather than complex mathematics, giving you the foundation to build modern AI applications.
What You'll Learn
- Understand the complete AI Engineering ecosystem, including Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, MCP, Embeddings, and AI Agents.
- Learn how to design effective prompts, work with tokens and context windows, generate structured outputs, and choose the right LLM for different applications.
- Understand how modern AI applications retrieve external knowledge, perform semantic search, and interact with tools using Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP).
- Build a strong conceptual foundation that prepares you to develop production-ready LLM-powered applications and pursue advanced AI Engineering topics.
About This Course
The demand for AI Engineers has exploded, but many developers struggle to understand how the different pieces of the AI ecosystem fit together.
This course is designed to give you a strong conceptual foundation in AI Engineering without overwhelming you with unnecessary theory.
You'll begin by understanding what an AI Engineer actually does and how Generative AI is transforming software development. From there, you'll explore Large Language Models (LLMs), how they work, their lifecycle, and how to choose the right model for different use cases.
Next, you'll master Prompt Engineering by learning the building blocks of effective prompts, including system prompts, user prompts, context, and output instructions. You'll also understand tokens, context windows, structured outputs, and proven prompting techniques used in real-world applications.
The course then dives into Retrieval-Augmented Generation (RAG), embeddings, semantic search, and the Model Context Protocol (MCP), explaining how AI applications access external knowledge and interact with tools.
Finally, you'll learn what AI Agents are, how they differ from traditional workflows, and explore the most popular agent frameworks used to build autonomous AI systems.
By the end of this course, you'll have a clear mental model of modern AI Engineering and be ready to start building production-ready AI applications or continue with more advanced topics.
Whether you're a software engineer, backend developer, DevOps engineer, student, or tech enthusiast, this course will give you the knowledge needed to confidently enter the world of AI Engineering.