LLM-Interview-Questions-and-Answers: 100 LLM interview questions with answers (github.com)

A new resource titled "LLM-Interview-Questions-and-Answers" has been launched, providing a compilation of 100 interview questions and answers specifically tailored for aspiring LLM engineers. This extensive collection covers critical topics like positional embeddings, tokenization, self-attention mechanisms, and inference efficiency. In addition to the Q&A format, the resource features over 25 prompt engineering techniques and a categorized toolkit that includes 120+ LLM libraries and survey papers. This initiative is significant for the AI/ML community as it serves both as an educational tool for those preparing for LLM-related roles and a reference for experienced professionals looking to deepen their understanding of LLM architectures and operational efficiencies. By addressing key concepts such as the impact of tokenization on model performance and the benefits of various decoding strategies, the repository equips users with essential knowledge to navigate the complexities of LLM systems. The inclusion of guidelines for optimizing inference and addressing memory requirements also highlights practical applications for deploying LLMs in real-time scenarios.