REPOGEO REPORT · LITE

gkamradt/LLMTest_NeedleInAHaystack

Default branch main · commit 021385d6 · scanned 6/26/2026, 10:22:01 AM

GitHub: 2,326 stars · 247 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

22 /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

1 / 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

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

OVERALL DIRECTION

hightopics#1

Add specific topics to improve category visibility

Why:

COPY-PASTE FIX

llm-evaluation, llm-benchmarking, long-context, retrieval-augmented-generation, rag, llm-testing, context-window, generative-ai

highreadme#2

Reposition the README H1 and opening sentence to clearly state its function

Why:

CURRENT

# Needle In A Haystack

> Pressure-test LLM long-context retrieval. Now in v2.

COPY-PASTE FIX

# Needle In A Haystack: LLM Long-Context Retrieval Evaluation

> A comprehensive tool to pressure-test and benchmark Large Language Models' long-context retrieval accuracy and multi-step reasoning capabilities. Now in v2.

mediumlicense#3

Clarify the existing license in the README

Why:

COPY-PASTE FIX

## License

This project is licensed under [describe your license here, e.g., 'a custom license combining elements of X and Y']. Please refer to the `LICENSE` file for full details.

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 gkamradt/LLMTest_NeedleInAHaystack

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DeepEval

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

DeepEval · recommended 2×
LongBench · recommended 1×
NarrativeQA · recommended 1×
QMSum · recommended 1×
HotpotQA · recommended 1×

CATEGORY QUERY
How to evaluate large language model long-context retrieval accuracy across various depths?
you: not recommended
AI recommended (in order):
1. LongBench
2. NarrativeQA
3. QMSum
4. HotpotQA
5. Prodigy
6. Label Studio
7. DPR
8. BM25
9. Ragas
10. LangChain Evaluation Module
11. DeepEval
12. LLM-Evals (from OpenAI, adaptable)
13. GPT-4
14. scikit-learn
15. NLTK
16. SpaCy
17. transformers
AI recommended 17 alternatives but never named gkamradt/LLMTest_NeedleInAHaystack. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help benchmark LLM performance for multi-step reasoning over extended contexts?
you: not recommended
AI recommended (in order):
1. MT-Bench
2. AlpacaEval
3. Prometheus
4. RAGAS
5. LangSmith
6. DeepEval
7. Amazon Mechanical Turk
8. Appen
9. Scale AI
10. Sentence-BERT
11. OpenAI Embeddings
12. Big Bench Hard
AI recommended 12 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 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?
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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MARKDOWN (README)

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gkamradt/LLMTest_NeedleInAHaystack — 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