km1994/LLMs_interview_notes resource

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2601 stars · 171 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated collection of interview questions and study notes for large language model (LLM) algorithm engineer positions, compiled from the authors' personal interview experience. It covers topics like transformer architecture, attention variants, layer normalization, and training objectives, with answers hosted externally.

Use cases

• prepare for LLM algorithm engineer interviews
• study large language model fundamentals
• review transformer and attention mechanism concepts
• find common LLM interview questions with answers
• brush up on deep learning theory before job interviews

When to choose

• you are interviewing for an LLM or NLP algorithm engineer role
• you want a structured question bank covering LLM theory and architecture details
• you prefer community-curated interview notes in Chinese

When to avoid

• you need runnable code or a software library rather than study notes
• you want fully self-contained answers, since many link to an external paid community
• you need up-to-date coverage of models released after late 2024

Facets

learning-resource · maturity active

documentation developer-tools large-language-models machine-learning tutorials education cross-platform llm-interview interview-preparation study-notes chinese-language algorithm-engineer

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markdown · JSON · MCP: product_card(name="km1994/LLMs_interview_notes")