# aceliuchanghong/FAQ_Of_LLM_Interview

大模型算法岗面试题(含答案):常见问题和概念解析 "大模型面试题"、"算法岗面试"、"面试常见问题"、"大模型算法面试"、"大模型应用基础"

Repository: https://github.com/aceliuchanghong/FAQ_Of_LLM_Interview
Canonical: https://ross.abutalabs.com/products/faq_of_llm_interview
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-07-20T14:33:48+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 93, release rhythm 35, longevity 64
- inputs: {"age_days": 903, "days_push": 44, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2009, forks 135 (observed 2026-08-28T04:06:04.935045+00:00)

## What it is
A curated FAQ-style collection of large language model algorithm interview questions with answers, covering math foundations, Transformer architectures, RAG, reinforcement learning, and agent systems. It is a Chinese-language study resource maintained as Markdown notes and Jupyter notebooks.

## Use cases
- prepare for LLM algorithm engineer interviews
- review transformer and attention mechanism concepts
- study RAG and vector database interview questions
- learn reinforcement learning basics like PPO and GRPO for interviews
- brush up on math foundations for machine learning interviews
- find common LLM interview questions with answers

## When to choose
- you are preparing for a large model algorithm role interview
- you want a concise question-and-answer review of LLM concepts
- you prefer curated study notes over full courses

## When to avoid
- you need hands-on production code or a runnable framework
- you need English-language interview material
- you want a structured curriculum with exercises and grading

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: large-language-models, machine-learning, tutorials, education
- platform: python
- tags: llm-interview, interview-preparation, faq, study-notes, jupyter-notebook, chinese-language

## Member repositories
- aceliuchanghong/FAQ_Of_LLM_Interview (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:04.935045+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:01:15.354846+00:00, confidence not recorded.
  - readme: https://github.com/aceliuchanghong/FAQ_Of_LLM_Interview (fetched 2026-08-28T04:06:04.935045+00:00, sha bd6efdf083f8)
- Data as of 2026-08-30T08:39:29.467469+00:00.