RRepoGEO

REPOGEO REPORT · LITE

gabriben/awesome-generative-information-retrieval

Default branch main · commit 1b1e242b · scanned 6/10/2026, 4:32:55 PM

GitHub: 721 stars · 50 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
10 /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
0 / 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 gabriben/awesome-generative-information-retrieval, 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 clear 'awesome list' description to the About section

    Why:

    COPY-PASTE FIX
    A curated list of papers, datasets, tools, and resources focusing on the intersection of generative AI and information retrieval, including RAG, LLM memory, and generative recommendation.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, choosing a standard open-source license like MIT or Apache-2.0 to clarify usage rights for contributors and users.
  • mediumreadme#3
    Explicitly state the repo is an 'awesome list' in the README's opening sentence

    Why:

    CURRENT
    Conversational models started to be able to access the web or backup their claims with sources (a.k.a. attribution). These chatbots are thus arguably information retrieval machines, competing against or even substituing traditional search engines. We would like to dedicate a space to these models but also to the more general field of generative information retrieval. We tentatively devide the field in two main topics: **Grounded Answer Generation** and **Generative Document Retrieval**. We also include generative recommendation, generative grounded summarization etc.
    COPY-PASTE FIX
    This is an awesome list dedicated to the rapidly evolving field of generative information retrieval. Conversational models started to be able to access the web or backup their claims with sources (a.k.a. attribution). These chatbots are thus arguably information retrieval machines, competing against or even substituing traditional search engines. We tentatively devide the field in two main topics: **Grounded Answer Generation** and **Generative Document Retrieval**. We also include generative recommendation, generative grounded summarization etc.

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 gabriben/awesome-generative-information-retrieval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Haystack · recommended 1×
  4. OpenAI API · recommended 1×
  5. Anthropic Claude · recommended 1×
  • CATEGORY QUERY
    How to build AI chatbots that provide accurate, attributed answers from external sources?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI API
    5. Anthropic Claude
    6. Google Gemini
    7. PyPDF2
    8. BeautifulSoup
    9. text-embedding-ada-002
    10. Pinecone
    11. Weaviate
    12. Chroma
    13. Azure AI Search
    14. Azure OpenAI Service
    15. Google Cloud Vertex AI Search and Conversation
    16. Vertex AI
    17. Amazon Kendra
    18. Amazon Bedrock
    19. AI21 Labs Jurassic
    20. Amazon Titan

    AI recommended 20 alternatives but never named gabriben/awesome-generative-information-retrieval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for generative AI to improve information retrieval and search results?
    you: not recommended
    AI recommended (in order):
    1. Google's MUM
    2. OpenAI's GPT-4
    3. Cohere's Command models
    4. Google's BERT
    5. Google's MUM embeddings
    6. OpenAI's text-embedding-ada-002
    7. Sentence-BERT (SBERT)
    8. Google's Featured Snippets
    9. Anthropic's Claude
    10. Google's personalized search
    11. OpenAI's GPT-3.5
    12. Google Bard
    13. Microsoft Copilot
    14. OpenAI's ChatGPT
    15. Llama 2
    16. Falcon
    17. Mistral AI's Mistral 7B

    AI recommended 17 alternatives but never named gabriben/awesome-generative-information-retrieval. 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 gabriben/awesome-generative-information-retrieval?
    pass
    AI did not name gabriben/awesome-generative-information-retrieval — 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 gabriben/awesome-generative-information-retrieval in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name gabriben/awesome-generative-information-retrieval — 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?

  • In one sentence, what problem does the repo gabriben/awesome-generative-information-retrieval solve, and who is the primary audience?
    pass
    AI did not name gabriben/awesome-generative-information-retrieval — 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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gabriben/awesome-generative-information-retrieval — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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