RRepoGEO

REPOGEO REPORT · LITE

lizhe2004/Awesome-LLM-RAG-Application

Default branch main · commit a0ffc2d7 · scanned 5/11/2026, 12:27:57 PM

GitHub: 1,637 stars · 111 forks

AI VISIBILITY SCORE
17 /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
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 lizhe2004/Awesome-LLM-RAG-Application, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, llm, rag, large-language-models, retrieval-augmented-generation, llm-applications, rag-applications, llm-resources, curated-list
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file with the MIT License text.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    Set the homepage URL to 'https://github.com/lizhe2004/Awesome-LLM-RAG-Application'

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 lizhe2004/Awesome-LLM-RAG-Application
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Weaviate · recommended 2×
  5. Faiss · recommended 1×
  • CATEGORY QUERY
    What are the best open-source tools for building robust RAG applications?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Faiss
    5. Weaviate
    6. Qdrant

    AI recommended 6 alternatives but never named lizhe2004/Awesome-LLM-RAG-Application. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I mitigate hallucinations and improve retrieval accuracy in my LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Pinecone
    5. Weaviate
    6. Chroma
    7. Cohere Rerank
    8. Sentence-BERT
    9. Hugging Face Transformers

    AI recommended 9 alternatives but never named lizhe2004/Awesome-LLM-RAG-Application. 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 lizhe2004/Awesome-LLM-RAG-Application?
    pass
    AI did not name lizhe2004/Awesome-LLM-RAG-Application — 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 lizhe2004/Awesome-LLM-RAG-Application in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lizhe2004/Awesome-LLM-RAG-Application 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 lizhe2004/Awesome-LLM-RAG-Application solve, and who is the primary audience?
    pass
    AI did not name lizhe2004/Awesome-LLM-RAG-Application — 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?

Embed your GEO score

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lizhe2004/Awesome-LLM-RAG-Application — 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