RRepoGEO

REPOGEO REPORT · LITE

microsoft/RAG_Hack

Default branch main · commit cc812281 · scanned 6/15/2026, 12:57:27 PM

GitHub: 518 stars · 115 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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 microsoft/RAG_Hack, 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
  • highhomepage#1
    Add the 'Reactor series home page' URL to the repo's homepage field

    Why:

    COPY-PASTE FIX
    [URL of the Reactor series home page mentioned in the README]
  • highreadme#2
    Reposition the README's opening statement to clarify its event nature

    Why:

    CURRENT
    🛠️ Build, innovate, and #Hacktogether! 🛠️
It's time to start building AI applications using the power of RAG (Retrieval Augmented Generation). 🤖 + 📚 = 🔥
    COPY-PASTE FIX
    🛠️ Build, innovate, and #Hacktogether! 🛠️ Welcome to RAGHack, a hands-on learning event and hackathon focused on building Retrieval Augmented Generation (RAG) applications with Azure AI.
  • mediumtopics#3
    Add more specific topics to reinforce its identity as a learning resource and Azure focus

    Why:

    CURRENT
    ai, azure, hackathon, rag, streams
    COPY-PASTE FIX
    ai, azure, hackathon, rag, streams, learning-event, azure-ai, tutorials, workshop

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 microsoft/RAG_Hack
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
run-llama/llama_index
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. run-llama/llama_index · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. deepset-ai/haystack · recommended 1×
  4. bclavie/RAGatouille · recommended 1×
  5. opensearch-project/OpenSearch · recommended 1×
  • CATEGORY QUERY
    How can I build retrieval augmented generation applications using various data sources and popular frameworks?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. LangChain (langchain-ai/langchain)
    3. Haystack (deepset-ai/haystack)
    4. RAGatouille (bclavie/RAGatouille)
    5. OpenSearch (opensearch-project/OpenSearch)
    6. Elasticsearch (elastic/elasticsearch)
    7. Weaviate (weaviate/weaviate)

    AI recommended 7 alternatives but never named microsoft/RAG_Hack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical tutorials on developing retrieval augmented generation systems across multiple languages.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Hugging Face Transformers
    3. Hugging Face Datasets
    4. LlamaIndex
    5. Haystack
    6. Gradio
    7. Streamlit

    AI recommended 7 alternatives but never named microsoft/RAG_Hack. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 microsoft/RAG_Hack?
    pass
    AI did not name microsoft/RAG_Hack — 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 microsoft/RAG_Hack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/RAG_Hack 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 microsoft/RAG_Hack solve, and who is the primary audience?
    pass
    AI named microsoft/RAG_Hack explicitly

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

Embed your GEO score

Drop this badge into the README of microsoft/RAG_Hack. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/microsoft/RAG_Hack"><img src="https://repogeo.com/badge/microsoft/RAG_Hack.svg" alt="RepoGEO" /></a>
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microsoft/RAG_Hack — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite