Introduction to Large Language Models

Introduction to Large Language Models#

Overview#

Welcome to Introduction to Large Language Models, a course designed to enhance your understanding of modern Large Language Models (LLMs) and how they work. This course takes a hands-on approach, combining conceptual slides with interactive Jupyter notebooks to guide you from the fundamentals of machine learning and neural networks through to the architecture underlying today’s state-of-the-art models. You will finish the session equipped with practical skills for working with LLMs effectively, including how to interpret tokenization, understand semantic embeddings, and craft effective prompts.

Course Objectives#

By the end of this course, participants will be able to:

  • Describe what an LLM does at a high level and explain the concept of next-token prediction

  • Trace the key developments in machine learning and neural networks that led to modern LLMs

  • Explain the core components of a transformer-based LLM: tokenization, embeddings, and attention

  • Understand important practical concepts such as statelessness, context windows, and model parameters

  • Explore how text is tokenized and why token counts matter for cost and context limits

  • Visualize and interpret text embeddings as representations of semantic meaning

  • Apply prompt engineering techniques — including few-shot learning and chain-of-thought reasoning — to improve model outputs

  • Control LLM output behaviour by tuning temperature and sampling parameters

Pre-requisite knowledge#

Participants should have completed Introduction to Machine Learning, or have equivalent prior experience, before attending this course.

Participants should also have access to a Python environment for the interactive notebook activities.

Resources#

  • Instructions on configuring a Python environment with Jupyter Notebooks can be found in the Introduction to Python setup guide.

  • Alternatively, the interactive sessions can be in a Google Colab session here. If this is preferred, participants should ensure they can log into a Colab session, upload and run the example notebook.

Running the interactive LLM notebooks

The website build does not execute the interactive LLM notebooks, and the standard CfRR build environment does not need to install the LLM-specific model and API packages.

If you are running these notebooks locally with Poetry, install the optional LLM dependency group first:

poetry install --with llm

If you are running the notebooks in a separate Jupyter or Colab environment, install the notebook-specific packages there instead:

%pip install tiktoken transformers sentence-transformers huggingface-hub python-dotenv

The practical prompting notebook can send prompts to Hugging Face Inference Providers when hosted inference is enabled. Set a Hugging Face token as HF_TOKEN only in your local environment, and do not submit personal, confidential, unpublished, sensitive, or institutionally restricted data to hosted inference services unless you have explicit approval.