Essential interview questions designed for AI enthusiasts and professionals focusing on Large Language Models (LLMs).

The content systematically covers the foundational architectural elements of LLMs, explaining core concepts such as tokenization, the attention mechanism, and the function of the context window.

It differentiates advanced fine-tuning techniques like LoRA versus QLoRA and details sophisticated generation strategies, including beam search and temperature control.

Furthermore, the document addresses critical training mathematics, discussing topics like cross-entropy loss and the application of the chain rule in gradient computation. The resource concludes by reviewing modern applications like Retrieval-Augmented Generation (RAG) and the significant challenges LLMs face in real-world deployment.