RRepoGEO

REPOGEO REPORT · LITE

sunnweiwei/RankGPT

Default branch main · commit 0d62bc38 · scanned 6/13/2026, 7:27:51 AM

GitHub: 668 stars · 65 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 sunnweiwei/RankGPT, 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
  • highreadme#1
    Reposition the README's opening to highlight RankGPT as an award-winning LLM re-ranking method.

    Why:

    CURRENT
    Code for paper "Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent" This project aims to explore generative LLMs such as ChatGPT and GPT-4 for relevance ranking in Information Retrieval (IR).
    COPY-PASTE FIX
    RankGPT is an EMNLP 2023 Outstanding Paper Award-winning method that leverages large language models (LLMs) like ChatGPT and GPT-4 for state-of-the-art relevance re-ranking in Information Retrieval (IR).
  • mediumtopics#2
    Add more specific topics related to generative AI re-ranking methods.

    Why:

    CURRENT
    chatgpt, information-retrieval, large-language-models, reranking
    COPY-PASTE FIX
    chatgpt, information-retrieval, large-language-models, reranking, generative-ai, llm-reranking, emnlp-outstanding-paper
  • mediumcomparison#3
    Add a 'Key Differentiators' section to the README.

    Why:

    COPY-PASTE FIX
    ## Key Differentiators
Unlike traditional fine-tuning approaches or generic LLM APIs, RankGPT leverages **off-the-shelf, general-purpose Large Language Models (LLMs)** (e.g., GPT-3.5, GPT-4) for **direct document re-ranking** primarily through **prompt engineering**. This eliminates the need for extensive task-specific fine-tuning on IR datasets, offering a flexible and powerful method for improving search relevance.

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 sunnweiwei/RankGPT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. huggingface/transformers · recommended 1×
  3. Cohere Rerank API · recommended 1×
  4. Pinecone · recommended 1×
  5. weaviate/weaviate · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models to improve the relevance of search results?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers (huggingface/transformers)
    3. Cohere Rerank API
    4. Pinecone
    5. Weaviate (weaviate/weaviate)
    6. Milvus (milvus-io/milvus)
    7. Qdrant (qdrant/qdrant)
    8. LangChain (langchain-ai/langchain)
    9. LlamaIndex (run-llama/llama_index)
    10. Elasticsearch (elastic/elasticsearch)
    11. Google Cloud Vertex AI

    AI recommended 11 alternatives but never named sunnweiwei/RankGPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for re-ranking search results using generative AI models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Anthropic Claude 3 Opus / Sonnet
    3. Google Gemini 1.5 Pro
    4. Cohere Command R+
    5. Hugging Face Transformers
    6. OpenAI API
    7. Ray RLlib

    AI recommended 7 alternatives but never named sunnweiwei/RankGPT. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 sunnweiwei/RankGPT?
    pass
    AI named sunnweiwei/RankGPT explicitly

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

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

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

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sunnweiwei/RankGPT — 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