RRepoGEO

REPOGEO REPORT · LITE

cvs-health/uqlm

Default branch main · commit a8defbfa · scanned 5/24/2026, 6:32:13 AM

GitHub: 1,156 stars · 125 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 cvs-health/uqlm, 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
  • highreadme#1
    Add a concise tagline at the very top of the README

    Why:

    COPY-PASTE FIX
    Add this as the absolute first line of your README:

A Python library for LLM hallucination detection and uncertainty quantification.
  • mediumreadme#2
    Introduce a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

UQLM specializes in response-level uncertainty quantification for LLM hallucination detection, offering a focused approach distinct from broader LLM evaluation frameworks like Ragas or DeepEval. While those tools provide comprehensive suites for various aspects of LLM performance, UQLM's strength lies in providing granular confidence scores based on state-of-the-art UQ techniques, making it ideal for integrating real-time hallucination checks into LLM applications.
  • lowreadme#3
    Ensure the primary descriptive sentence is immediately visible after the H1

    Why:

    CURRENT
    The README currently places badges and links between the H1 and the first descriptive sentence.
    COPY-PASTE FIX
    Move the sentence 'UQLM is a Python library for Large Language Model (LLM) hallucination detection using state-of-the-art uncertainty quantification techniques.' to appear directly after the H1, before any badges or other links.

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 cvs-health/uqlm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Scale AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Scale AI · recommended 2×
  2. Appen · recommended 2×
  3. Surge AI · recommended 2×
  4. Ragas · recommended 1×
  5. GPT-4 · recommended 1×
  • CATEGORY QUERY
    How can I detect and quantify hallucinations in large language models effectively?
    you: not recommended
    AI recommended (in order):
    1. Ragas
    2. GPT-4
    3. Claude Opus
    4. Gemini Advanced
    5. LangChain's evaluation modules
    6. FactGPT
    7. FactScore
    8. OpenAI Evals
    9. Scale AI
    10. Appen
    11. Surge AI
    12. Sentence-BERT
    13. OpenAI Embeddings

    AI recommended 13 alternatives but never named cvs-health/uqlm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate LLM reliability and estimate confidence scores for generated text?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Ragas (explodinggradients/ragas)
    3. DeepEval (confident-ai/deepeval)
    4. Phoenix (Arize-AI/phoenix)
    5. W&B Prompts (wandb/wandb)
    6. OpenAI Evals (openai/evals)
    7. Scale AI
    8. Appen
    9. Surge AI

    AI recommended 9 alternatives but never named cvs-health/uqlm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 cvs-health/uqlm?
    pass
    AI named cvs-health/uqlm explicitly

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

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

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

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cvs-health/uqlm — 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