RRepoGEO

REPOGEO REPORT · LITE

km1994/LLMs_interview_notes

Default branch main · commit 032afcb1 · scanned 5/25/2026, 11:12:46 PM

GitHub: 2,552 stars · 172 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface km1994/LLMs_interview_notes, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, interview-preparation, interview-questions, algorithm-engineer, machine-learning-interview, deep-learning-interview, ai-interview
  • mediumreadme#2
    Clarify README's opening sentence for AI understanding

    Why:

    CURRENT
    本项目是作者们根据个人面试和经验总结出的 大模型(LLMs)面试准备的学习笔记与资料，该资料目前包含 大模型(LLMs)各领域的 面试题积累。
    COPY-PASTE FIX
    本项目是作者们根据个人面试和经验总结出的 大模型(LLMs)算法工程师面试准备的学习笔记与资料，提供涵盖LLMs各领域的核心面试问题与解答。
  • lowhomepage#3
    Set the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/km1994/LLMs_interview_notes

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface km1994/LLMs_interview_notes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 1×
  2. QLoRA · recommended 1×
  3. AdamW · recommended 1×
  4. GLUE · recommended 1×
  5. SuperGLUE · recommended 1×
  • CATEGORY QUERY
    What are common interview questions for large language model algorithm engineers?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. QLoRA
    3. AdamW
    4. GLUE
    5. SuperGLUE
    6. MMLU
    7. HELM
    8. BIG-bench
    9. PyTorch
    10. TensorFlow

    AI recommended 10 alternatives but never named km1994/LLMs_interview_notes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find explanations and comparisons of different LLM activation functions and normalization techniques?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code (PWC)
    2. Distill.pub
    3. Jay Alammar's Blog
    4. DeepLearning.AI Courses
    5. Hugging Face
    6. Towards Data Science
    7. arXiv.org

    AI recommended 7 alternatives but never named km1994/LLMs_interview_notes. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of km1994/LLMs_interview_notes?
    pass
    AI did not name km1994/LLMs_interview_notes — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts km1994/LLMs_interview_notes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named km1994/LLMs_interview_notes explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo km1994/LLMs_interview_notes solve, and who is the primary audience?
    pass
    AI did not name km1994/LLMs_interview_notes — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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km1994/LLMs_interview_notes — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite