RRepoGEO

REPOGEO REPORT · LITE

Tongji-KGLLM/RAG-Survey

Default branch main · commit 89d56bd1 · scanned 5/27/2026, 3:18:15 AM

GitHub: 2,132 stars · 132 forks

AI VISIBILITY SCORE
30 /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
3 / 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 Tongji-KGLLM/RAG-Survey, 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 comprehensive and actively curated survey repository on Retrieval-Augmented Generation (RAG) for Large Language Models (LLMs), including papers, datasets, and frameworks.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    retrieval-augmented-generation, rag, large-language-models, llm, nlp, survey, research, artificial-intelligence, machine-learning
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the repository root to clarify usage rights.

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 Tongji-KGLLM/RAG-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. Papers With Code · recommended 1×
  4. ACL Anthology · recommended 1×
  5. Distill.pub · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive research on retrieval-augmented generation for large language models?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Papers With Code
    4. ACL Anthology
    5. Distill.pub
    6. Hugging Face Blog/Research
    7. OpenAI Research Blog/Papers

    AI recommended 7 alternatives but never named Tongji-KGLLM/RAG-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for building retrieval-augmented generation systems with large language models?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. DSPy
    6. LiteLLM
    7. OpenSearch

    AI recommended 7 alternatives but never named Tongji-KGLLM/RAG-Survey. 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 Tongji-KGLLM/RAG-Survey?
    pass
    AI named Tongji-KGLLM/RAG-Survey explicitly

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

  • If a team adopts Tongji-KGLLM/RAG-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Tongji-KGLLM/RAG-Survey 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 Tongji-KGLLM/RAG-Survey solve, and who is the primary audience?
    pass
    AI named Tongji-KGLLM/RAG-Survey explicitly

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

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Tongji-KGLLM/RAG-Survey — 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