AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents

What you'll learn
Project 1: Make AI-powered brochure generator that scrapes and navigates company websites intelligently.
Project 2: Build Multi-modal customer support agent for an airline with UI and function-calling.
Project 3: Develop Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
Project 4: Make AI that converts Python code to optimized C++, boosting performance by 60,000x!
Project 5: Build AI knowledge-worker using RAG to become an expert on all company-related matters.
Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
Project 7: Capstone Part B – Execute Fine-tuned open-source model to compete with Frontier in price prediction.
Project 8: Capstone Part C – Build Autonomous multi agent system collaborating with models to spot deals and notify you of special bargains.
Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows
Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task