RRepoGEO

REPOGEO REPORT · LITE

gkamradt/LLMTest_NeedleInAHaystack

Default branch main · commit 7b90d285 · scanned 5/15/2026, 5:22:47 PM

GitHub: 2,285 stars · 243 forks

AI VISIBILITY SCORE
62 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 gkamradt/LLMTest_NeedleInAHaystack, 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
    Reposition README opening to emphasize LLM comparison tool

    Why:

    CURRENT
    A simple 'needle in a haystack' analysis to test in-context retrieval ability of long context LLMs.
    COPY-PASTE FIX
    Needle In A Haystack is a robust tool for systematically comparing and evaluating the in-context retrieval ability of long context LLMs across various models and context lengths.
  • mediumreadme#2
    Clarify license status in README

    Why:

    COPY-PASTE FIX
    This project includes a LICENSE file. Please refer to it for specific terms, as it is not a standard SPDX template.

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
1 / 2
50% of queries surface gkamradt/LLMTest_NeedleInAHaystack
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
google-research/rouge-score
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. google-research/rouge-score · recommended 2×
  2. ELI5 (Explain Like I'm 5) · recommended 1×
  3. Natural Questions (NQ) · recommended 1×
  4. Multi-News · recommended 1×
  5. WikiAsp · recommended 1×
  • CATEGORY QUERY
    How to evaluate large language model performance on long context window retrieval tasks?
    you: #1
    AI recommended (in order):
    1. Needle in a Haystack (gkamradt/LLMTest_NeedleInAHaystack) ← you
    2. ELI5 (Explain Like I'm 5)
    3. Natural Questions (NQ)
    4. Multi-News
    5. WikiAsp
    6. ROUGE-N (google-research/rouge-score)
    7. ROUGE-L (google-research/rouge-score)
    8. BLEU (nltk/nltk)
    9. SQuAD evaluation script (rajpurkar/SQuAD-explorer)
    10. Sentence-BERT (UKPLab/sentence-transformers)
    11. OpenAI embeddings (openai/openai-python)
    12. LlamaIndex (run-llama/llama_index)
    13. LangChain (langchain-ai/langchain)
    14. Ragas (explodinggradients/ragas)
    15. Argilla (argilla-io/argilla)
    16. Scale AI
    17. Appen
    Show full AI answer
  • CATEGORY QUERY
    Tool for comparing retrieval accuracy of different LLMs with varying context lengths?
    you: not recommended
    AI recommended (in order):
    1. Ragas
    2. LlamaIndex
    3. Haystack
    4. LangChain
    5. DeepEval
    6. Hugging Face Datasets
    7. Transformers

    AI recommended 7 alternatives but never named gkamradt/LLMTest_NeedleInAHaystack. 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 gkamradt/LLMTest_NeedleInAHaystack?
    pass
    AI named gkamradt/LLMTest_NeedleInAHaystack explicitly

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

  • If a team adopts gkamradt/LLMTest_NeedleInAHaystack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gkamradt/LLMTest_NeedleInAHaystack 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 gkamradt/LLMTest_NeedleInAHaystack solve, and who is the primary audience?
    pass
    AI did not name gkamradt/LLMTest_NeedleInAHaystack — likely talking about a different project

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

Embed your GEO score

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gkamradt/LLMTest_NeedleInAHaystack — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
gkamradt/LLMTest_NeedleInAHaystack — RepoGEO report