AI Agents & LLM Engineering: From Zero to Production

About this course

This course contains the use of artificial intelligence.

You can call an LLM. Now what?

Sending a prompt and getting text back is the easy part. You have probably done it already. The hard part is everything after it: getting a model to search the web, read your documents, use real tools, remember what happened, and keep going until the job is actually done.

That middle part, between "I called an API" and "I shipped something that works", is where most people get stuck. It is also the part almost nobody teaches properly. This course is that part.

You will build four working agents

Not toy demos. Four complete projects you write line by line, run on your own machine, and keep in your portfolio.

Project 1, Research Agent: it takes a question, plans what to look up, searches, tracks its sources, and writes a report with real numbered citations. You will build the search tool, the planner, and the synthesis step yourself.

Project 2, RAG Support Chatbot: it answers questions from your own documents instead of making things up. You will do embeddings by hand first, cosine similarity, chunking, semantic search, and only then switch to a vector database, so you know exactly what it is doing for you. It also learns to say "I don't know" when the answer isn't in your documents.

Project 3, Multi-Agent Content Team: a planner, a writer, and a critic that review each other's work in a revision loop. You will build the state machine by hand first, feel exactly why that hurts, and then rebuild it in LangGraph, with memory, streaming, and a human-in-the-loop approval step.

Project 4, MCP Personal Assistant: you will write your own MCP server with real tools, resources, and prompts, connect it to an agent you wrote yourself, add the official filesystem server alongside it, and then plug the whole thing into Claude Desktop.

One idea underneath all of them

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