Large Language Models, or LLMs, are the core engines behind modern AI systems such as ChatGPT, Claude, and Llama. In this module, you will build a strong mental model of how these systems actually work, moving beyond surface level usage into architectural understanding. You will learn how LLMs are trained, how they represent language internally, and what infrastructure is required to build and run them responsibly.
This foundation is critical for anyone who wants to fine-tune models, evaluate outputs, or work professionally on AI data and model development projects, especially in African contexts where compute, language diversity, and data availability matter.
Learning Objectives
By the end of this module, you will be able to:
- Explain how the Transformer architecture works at a conceptual level
- Describe the differences between pre-training, fine-tuning, and instruction tuning
- Identify how tokenization and embeddings represent language numerically
- Compare major LLM model families and their use cases
- Explain basic compute and infrastructure requirements for LLM work
- Set up a functional local or cloud-based LLM development environment