RRepoGEO

REPOGEO REPORT · LITE

limouren2000/llms-dev-study

Default branch main · commit 25bc2aab · scanned 6/2/2026, 4:57:56 AM

GitHub: 748 stars · 62 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 limouren2000/llms-dev-study, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A fast-track learning path and interview guide for Large Language Model (LLM) application development, focusing on RAG and Agent techniques for job seekers.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, rag, retrieval-augmented-generation, agent, llm-development, interview-prep, learning-path, ai-applications, langchain
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0) to the repository root.

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 limouren2000/llms-dev-study
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. DeepLearning.AI · recommended 2×
  4. Hugging Face Transformers · recommended 2×
  5. AI Coffee Break with Letitia · recommended 2×
  • CATEGORY QUERY
    What are the best resources for quickly learning RAG and Agent development for job interviews?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. DeepLearning.AI
    4. ChatGPT API
    5. Hugging Face Transformers
    6. Hugging Face Datasets
    7. OpenAI API
    8. AI Coffee Break with Letitia
    9. Greg Kamradt

    AI recommended 9 alternatives but never named limouren2000/llms-dev-study. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical tutorials and project examples to build RAG-based LLM applications quickly.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. datasets Library
    5. DeepLearning.AI
    6. Pinecone
    7. Weaviate
    8. AI Coffee Break with Letitia
    9. Greg Kamradt

    AI recommended 9 alternatives but never named limouren2000/llms-dev-study. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 limouren2000/llms-dev-study?
    pass
    AI named limouren2000/llms-dev-study explicitly

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

  • If a team adopts limouren2000/llms-dev-study in production, what risks or prerequisites should they evaluate first?
    pass
    AI named limouren2000/llms-dev-study 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 limouren2000/llms-dev-study solve, and who is the primary audience?
    pass
    AI did not name limouren2000/llms-dev-study — 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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limouren2000/llms-dev-study — 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