RRepoGEO

REPOGEO REPORT · LITE

stanford-futuredata/ARES

Default branch main · commit c7c9018a · scanned 6/13/2026, 11:07:46 PM

GitHub: 715 stars · 69 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 stanford-futuredata/ARES, 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 specific topics for RAG evaluation

    Why:

    COPY-PASTE FIX
    ["rag-evaluation", "retrieval-augmented-generation", "llm-evaluation", "nlp", "machine-learning", "ai-evaluation", "synthetic-data"]
  • highreadme#2
    Add a concise, prominent tagline to the README's opening

    Why:

    CURRENT
    The current README structure places the first descriptive paragraph after a table of contents and badge section.
    COPY-PASTE FIX
    Insert the following sentence immediately after the H2, before the table of contents: 'ARES is the definitive automated framework for evaluating Retrieval-Augmented Generation (RAG) systems, ensuring robust assessment of context relevance, answer faithfulness, and answer relevance.'
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

ARES stands out from other RAG evaluation frameworks like Ragas, TruLens, and DeepEval by offering [mention 1-2 key differentiators, e.g., its unique Prediction-Powered Inference (PPI) for statistical confidence, or its comprehensive synthetic data generation capabilities]. While other tools provide valuable metrics, ARES's automated approach minimizes human annotation needs and provides statistically robust evaluations.

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 stanford-futuredata/ARES
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ragas-AI/ragas
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ragas-AI/ragas · recommended 1×
  2. truera/trulens · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. confident-ai/deepeval · recommended 1×
  5. Phoenix · recommended 1×
  • CATEGORY QUERY
    How can I automatically evaluate the performance and quality of my RAG system?
    you: not recommended
    AI recommended (in order):
    1. Ragas (Ragas-AI/ragas)
    2. TruLens (truera/trulens)
    3. LangChain (langchain-ai/langchain)
    4. DeepEval (confident-ai/deepeval)
    5. Phoenix
    6. LlamaIndex (run-llama/llama_index)
    7. Galileo

    AI recommended 7 alternatives but never named stanford-futuredata/ARES. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help assess RAG context relevance and answer faithfulness without human annotation?
    you: not recommended
    AI recommended (in order):
    1. RAGAS
    2. DeepEval
    3. LangChain Evaluation
    4. LlamaIndex Evaluation
    5. Galileo Evaluate
    6. OpenAI Evals
    7. Sentence Transformers
    8. BERTScore
    9. BLEURT

    AI recommended 9 alternatives but never named stanford-futuredata/ARES. 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 stanford-futuredata/ARES?
    pass
    AI named stanford-futuredata/ARES explicitly

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

  • If a team adopts stanford-futuredata/ARES in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stanford-futuredata/ARES 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 stanford-futuredata/ARES solve, and who is the primary audience?
    pass
    AI named stanford-futuredata/ARES 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 stanford-futuredata/ARES. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/stanford-futuredata/ARES.svg)](https://repogeo.com/en/r/stanford-futuredata/ARES)
HTML
<a href="https://repogeo.com/en/r/stanford-futuredata/ARES"><img src="https://repogeo.com/badge/stanford-futuredata/ARES.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

stanford-futuredata/ARES — 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