RRepoGEO

REPOGEO REPORT · LITE

amazon-science/RAGChecker

Default branch main · commit 6091f08c · scanned 7/1/2026, 7:07:33 PM

GitHub: 1,100 stars · 89 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
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 amazon-science/RAGChecker, 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
  • mediumhomepage#1
    Set the repository homepage URL to the paper link

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2408.08067
  • mediumreadme#2
    Emphasize RAGChecker's reference-free evaluation as a key differentiator in the README

    Why:

    CURRENT
    Fine-grained Evaluation**: Utilizes `claim-level entailment` operations for fine-grained evaluation.
    COPY-PASTE FIX
    Fine-grained Evaluation**: RAGChecker offers **reference-free evaluation of RAG system generation quality**, assessing the consistency and faithfulness of generated answers against retrieved documents without needing human-written references. It utilizes `claim-level entailment` operations for fine-grained analysis.

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 amazon-science/RAGChecker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ragas
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ragas · recommended 2×
  2. DeepEval · recommended 2×
  3. Label Studio · recommended 2×
  4. LangChain Evaluation · recommended 1×
  5. LlamaIndex Evaluation · recommended 1×
  • CATEGORY QUERY
    What are the best tools for diagnosing and evaluating RAG system performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain Evaluation
    2. Ragas
    3. LlamaIndex Evaluation
    4. Arize AI
    5. Weights & Biases
    6. DeepEval
    7. Label Studio
    8. Argilla

    AI recommended 8 alternatives but never named amazon-science/RAGChecker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform fine-grained analysis to improve my RAG pipeline's retriever and generator?
    you: not recommended
    AI recommended (in order):
    1. Ragas
    2. LangSmith
    3. DeepEval
    4. Phoenix
    5. W&B Prompts
    6. LlamaIndex
    7. Label Studio
    8. Prodigy

    AI recommended 8 alternatives but never named amazon-science/RAGChecker. 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 amazon-science/RAGChecker?
    pass
    AI named amazon-science/RAGChecker explicitly

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

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

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

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MARKDOWN (README)
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amazon-science/RAGChecker — 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