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

Requirements

• Familiarity with Python. This course will not cover Python basics and is completed in Python.
• A PC with an internet connection is required. Either Mac (Linux) or Windows.
• We recommend that you allocate around $5 for API costs to work with frontier models. However, you can complete the course using open-source models if you prefer.

Who this course is for

• Aspiring AI engineers and data scientists eager to break into the field of Generative AI and LLMs.
• Professionals looking to upskill and stay competitive in the rapidly evolving AI landscape.
• Developers interested in building advanced AI applications with practical, hands-on experience.
• Individuals seeking a career transition or aiming to enhance productivity through LLM-built frameworks.