RRepoGEO

REPOGEO REPORT · LITE

AkariAsai/self-rag

Default branch main · commit 1fcdc420 · scanned 5/26/2026, 3:38:05 AM

GitHub: 2,382 stars · 224 forks

AI VISIBILITY SCORE
35 /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
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 AkariAsai/self-rag, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Emphasize Self-RAG's role as a novel framework implementation in the README

    Why:

    CURRENT
    This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection (ICLR 2024, Oral top 1%) by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.
    COPY-PASTE FIX
    This repository provides the original implementation of **Self-RAG**, a novel framework for Retrieval-Augmented Generation (RAG) that enables Large Language Models to learn to retrieve, generate, and critique through self-reflection. Presented at ICLR 2024 (Oral top 1%), Self-RAG significantly enhances the factuality and quality of LLM generations.
  • mediumreadme#2
    Add a 'Comparison with Traditional RAG' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Traditional RAG
    
    Unlike traditional Retrieval-Augmented Generation (RAG) systems where an LLM passively consumes retrieved context, Self-RAG empowers the LLM to actively retrieve information on demand, generate responses, and critically evaluate its own output using 'reflection tokens'. This self-correction mechanism allows for dynamic adaptation and significantly improves generation quality and factuality beyond static RAG approaches or general LLM frameworks.

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 AkariAsai/self-rag
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/llama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/llama · recommended 2×
  2. GPT-4 · recommended 1×
  3. Claude 3 Opus · recommended 1×
  4. Gemini 1.5 Pro · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How to implement an LLM that can critique its own generated responses for accuracy?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Llama 3 (facebookresearch/llama)
    4. Gemini 1.5 Pro
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. Pinecone
    8. Weaviate (weaviate/weaviate)
    9. Chroma (chroma-core/chroma)
    10. Qdrant (qdrant/qdrant)
    11. OpenAI's `text-embedding-3-large`
    12. Cohere Embed v3
    13. Hugging Face's `sentence-transformers/all-MiniLM-L6-v2`
    14. OpenAI Fine-tuning API
    15. gpt-3.5-turbo
    16. Hugging Face Transformers (huggingface/transformers)
    17. Llama 2 (facebookresearch/llama)
    18. Mistral (mistralai/mistral-src)
    19. Falcon (tiiuae/falcon-7b)
    20. PEFT (huggingface/peft)
    21. GPT-4o

    AI recommended 21 alternatives but never named AkariAsai/self-rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework allows an LLM to dynamically retrieve information and self-correct generations?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Microsoft Guidance
    5. DSPy
    6. Auto-GPT
    7. BabyAGI

    AI recommended 7 alternatives but never named AkariAsai/self-rag. 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 AkariAsai/self-rag?
    pass
    AI named AkariAsai/self-rag explicitly

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

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

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

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AkariAsai/self-rag — 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