AI Engineer Bootcamp 2027: LLM, RAG, AI Agents, MCP (Python)

About this course

This course contains the use of artificial intelligence.

If you want to learn how to build a real AI product with ready-made language models, this course was made for that. An AI engineer usually does not train models from scratch; they turn ready models such as OpenAI, Gemini, Claude or a local model running on your own computer into a product with their own code, and fine-tune when needed. University "AI engineering" degrees cover the whole field; in this course we use the term in its current industry sense: the engineer who builds products on ready models. You learn it in Python, with small projects that each run on their own: prompt engineering, RAG, tool calling, AI agents (agentic AI), LangGraph and MCP; from setup to deployment in about ten hours. The course is beginner-level in AI: no AI or machine learning background is needed, we start from installing Python and the very first API call; basic programming knowledge (variables, loops, functions in any language) is enough, and two short "Python for AI" lessons cover the constructs we use.

How do we learn?

Every lesson teaches one concept and first shows the problem on screen: without a system message the model gives a long, messy answer; without conversation history it forgets your name; without RAG it makes things up; an agent without a step limit runs in circles; a poisoned PDF tricks the assistant. Then we add the few lines that fix the problem, repeat the same experiment and watch the problem go away. The code of every lesson is downloadable, and every lesson ends with a "run and watch" step: which command to type, what you will see on screen, and how to read the most common error messages.

What do we build?

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