Understand how LLM inference works, including tokenization, prefill and decode phases, KV cache, attention, sampling, and how transformer models generate text token by token in production systems.
A system-level overview of LLM architecture, including transformer blocks, encoder vs decoder models, attention mechanisms, training and inference flow, and how all components fit together in modern AI systems.
Understand the attention mechanism that powers Large Language Models (LLMs), including self-attention, query-key-value vectors, attention scores, multi-head attention, and why attention replaced recurrent neural networks.
Learn the key components of modern LLMs including tokenization, embeddings, transformers, attention, training, inference, and model parameters.
Learn what the context window is in LLMs, how token limits work, why context length matters, and how it impacts memory, reasoning, and real-world AI applications like RAG and chat systems.