AI Engineering Fundamentals: Build LLM Apps, RAG and Agents

About this course

This course contains the use of artificial intelligence.

You have used ChatGPT or Claude. Now you want to understand how they work and build with them. This course takes you from typing prompts into a chat box to working LLM apps that answer questions from your own documents, call tools, run as agents, and pass test sets you wrote yourself. Every idea is explained in plain words first, with a picture and a real example, so you are never left guessing what a term means.

It starts with a map of the whole field. In the first lesson you see every layer of an LLM app on one screen: the model, prompts and context, retrieval, tools and agents, evals and guardrails, and the app itself. Then you climb one layer at a time, and every section ends with something you can check.

This course is for beginners: developers new to AI, students, analysts, product people and career changers who can read simple Python. You do not need machine learning, math or any framework. It is not for engineers who already ship RAG and agents in production, and it is not a course on training models.

What makes it different: most beginner AI courses show a demo that worked once. Here every hard idea gets a drawn diagram, two examples and a counterexample. You learn to test your app with evals, run them in CI, trace every call, catch hallucinations and block prompt injection, the parts that decide whether an AI feature survives real users.

What is inside: Section 1, How LLMs actually work: the AI engineering stack, next-token prediction, temperature, tokens and context windows, your first API call, chat history, streaming with rate limits and retries, running open models locally with Ollama, and choosing a provider while keeping API keys safe. Section 2, Prompts and structured outputs: system prompts, few-shot examples, JSON Schema outputs, fixing broken prompts, reasoning prompts, images and PDFs as input, prompt templates and versioning,

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