LLM Engineering: Master AI & Large Language Models (LLMs)

Become an LLM Engineer in 8 weeks: Build and deploy 8 LLM apps, mastering Generative AI and key theoretical concepts.

Created by: Ligency Team
Helping Data Scientists Succeed

Created by: Ed Donner
AI startup co-founder and leader; Gen AI and LLM instructor

What Will I 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-tuneв 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.
• Design and develop a full solution to a given business problem by selecting, training and applying LLMs
• 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
• Solve problems by applying leading open-source platforms, frameworks and tools, including Hugging Face, Gradio and Weights & Biases
• State the common AI paradigms, and identify the types of business problems most suitable for each
• Define fundamental data science concepts around deep learning, including training vs inference, generalizing vs overfitting, and the key ideas behind the NN
• Describe core concepts such as Generative AI, LLMs and the Transformer Architecture, and discuss what can be achieved with state-of-the-art performance
• Explain how LLMs work in sufficient detail to be able to train and test them, apply them to new scenarios, and diagnose & fix common issues
• Implement LLM solutions in Python using frontier and open-source models with both APIs and direct inference
• Execute code to write documents, answer questions and generate images.

Requirements

• Familiarity with Python. This course will not cover Python basics and is completed in Python.

Target audience

• 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.