RRepoGEO

REPOGEO REPORT · LITE

amazon-science/RAGChecker

Default branch main · commit 6091f08c · scanned 5/20/2026, 4:51:47 AM

GitHub: 1,085 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    rag, rag-evaluation, llm-evaluation, nlp, diagnostic-tool, retrieval-augmented-generation, llm-ops, ai-observability, fine-grained-analysis
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2408.08067
  • lowreadme#3
    Refine README introduction to emphasize diagnostic depth

    Why:

    CURRENT
    RAGChecker is an advanced automatic evaluation framework designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It provides a comprehensive suite of metrics and tools for in-depth analysis of RAG performance.
    COPY-PASTE FIX
    RAGChecker is an advanced automatic evaluation framework designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. Unlike general observability platforms, it provides a comprehensive suite of fine-grained diagnostic metrics and tools for in-depth, claim-level analysis of RAG performance, empowering targeted improvements.

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
LangSmith
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangSmith · recommended 1×
  2. Phoenix by Arize AI · recommended 1×
  3. W&B Prompts · recommended 1×
  4. OpenTelemetry · recommended 1×
  5. Grafana Tempo · recommended 1×
  • CATEGORY QUERY
    How can I effectively evaluate and diagnose performance issues in my RAG pipeline?
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. Phoenix by Arize AI
    3. W&B Prompts
    4. OpenTelemetry
    5. Grafana Tempo
    6. Jaeger
    7. Elastic APM
    8. Prometheus
    9. Grafana
    10. Deepchecks LLM Eval

    AI recommended 10 alternatives but never named amazon-science/RAGChecker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide fine-grained diagnostic analysis for RAG retriever and generator components?
    you: not recommended
    AI recommended (in order):
    1. LangChain Plus (now LangSmith)
    2. Arize AI (Phoenix) (Arize-ai/phoenix)
    3. Weights & Biases (W&B Prompts)
    4. DeepEval (confident-ai/deepeval)
    5. OpenReplay (openreplay/openreplay)
    6. Grafana/Prometheus
    7. Elastic Stack (Elasticsearch, Logstash, Kibana)

    AI recommended 7 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?

Embed your GEO score

Drop this badge into the README of amazon-science/RAGChecker. 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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amazon-science/RAGChecker — 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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