Overview of Large Language Models (LLMs) Introduction to LLMs and their capabilities in text generation and natural language processing.

Transformers Discussion of transformer architecture, which forms the basis for LLMs. Presentation of the attention mechanism as a key element in LLMs, explaining how it allows models to selectively focus on specific information. Examples of attention model usage in LLMs.

Various Methods for Customizing LLMs Introduction to different techniques used to customize LLMs for specific tasks, domains, and data.

LLM Fine-Tuning The process of further training pre-trained LLMs on specific data. Utilizing fine-tuning to adapt the model for specific applications (practical exercises).

Retrieval Augmented Generation (RAG) Explanation of the RAG approach, which combines text generation with information retrieval from an internal document set. Practical examples of RAG usage. Examples of advanced techniques to enhance RAG performance (practical exercises).