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llm_interview_note is an open-source knowledge repository designed to help engineers prepare for interviews and deepen their understanding of large language models (LLMs). The project compiles structured notes, conceptual explanations, and curated interview questions related to modern NLP and generative AI systems. It covers fundamental topics such as the historical evolution of language models, tokenization methods, word embeddings, and the architectural foundations of transformer-based models. The repository also explores practical engineering concerns including distributed training strategies, dataset construction, model parameters, and scaling techniques used in large-scale machine learning systems. By organizing topics in a hierarchical documentation format, it enables readers to progress from basic NLP concepts to advanced topics like mixture-of-experts architectures and large-scale training frameworks.
Features
- Structured knowledge base for large language model concepts
- Coverage of transformer architectures and attention mechanisms
- Curated interview questions for machine learning and AI engineering roles
- Sections on distributed training methods and scaling strategies
- Hierarchical documentation format for progressive learning
- Educational explanations of NLP fundamentals such as tokenization and embeddings
Project Samples
