Learn how to build production-ready AI agents and RAG applications with Python. You’ll build 3 real LLM projects covering RAG, vector search, tool calling, autonomous agents, evaluation, security and deployment. You can use AI. But can you explain why it just did that? You paste a prompt, you get an answer, and it works. Until it doesn’t. Then the JSON breaks intermittently. The bill is four times what you estimated. The chatbot forgets its own instructions after ten messages. It cites a source that doesn’t exist. And you have no idea why — because nobody ever explained what’s actually happening underneath. This course closes that gap in under four hours. No research papers. No maths you’ll never use. No 20-hour runtime padded with theory. Every lecture ends with something you can apply the same day — and, unusually, with the actual cost and latency numbers.
By the end of this course you will be able to:
- Explain what an LLM is really doing — tokens, embeddings, context windows, temperature, and why hallucinations are a feature of the design, not a bug
- Write prompts that survive production — six patterns, structured output that never breaks your parser, and defences against prompt injection
- Build a RAG system over your own documents — chunking that doesn’t destroy meaning, hybrid search, re-ranking, citations, and per-user access control
- Ship an autonomous AI agent — the ReAct loop from scratch, tools with proper permission boundaries, memory, and guardrails that hold when prompts fail
- Deploy it properly — evaluation harness, 85% cost reduction, streaming, secure deployment, logging and monitoring
