RRepoGEO

REPOGEO REPORT · LITE

NirDiamant/Prompt_Engineering

Default branch main · commit fdf14f54 · scanned 5/29/2026, 6:22:58 AM

GitHub: 7,546 stars · 975 forks

AI VISIBILITY SCORE
28 /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
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 NirDiamant/Prompt_Engineering, 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
    Clarify README's opening to emphasize hands-on tutorials and practical implementations

    Why:

    CURRENT
    Welcome to one of the most extensive and dynamic collections of Prompt Engineering tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to advanced strategies for leveraging large language models.
    COPY-PASTE FIX
    Welcome to NirDiamant/Prompt_Engineering, one of the most extensive and dynamic collections of **22 hands-on Jupyter Notebook tutorials** for Prompt Engineering. This repository serves as a comprehensive, practical resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to advanced strategies for leveraging large language models.
  • highhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://diamant-ai.com/
  • mediumlicense#3
    Clarify the repository's license(s) in the README

    Why:

    COPY-PASTE FIX
    This repository is provided under [insert specific license name(s) here, e.g., 'a custom license' or 'a combination of licenses as detailed in the LICENSE file']. Please refer to the LICENSE file for full terms and conditions.

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 NirDiamant/Prompt_Engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepLearning.AI's "Prompt Engineering for Developers" Course
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepLearning.AI's "Prompt Engineering for Developers" Course · recommended 1×
  2. OpenAI's Official Prompt Engineering Guide · recommended 1×
  3. LearnPrompting.org · recommended 1×
  4. Google's "Introduction to Generative AI" on Coursera/Google Cloud Skills Boost · recommended 1×
  5. Hugging Face's Transformers Library · recommended 1×
  • CATEGORY QUERY
    How can I learn prompt engineering with practical, hands-on tutorials for large language models?
    you: not recommended
    AI recommended (in order):
    1. DeepLearning.AI's "Prompt Engineering for Developers" Course
    2. OpenAI's Official Prompt Engineering Guide
    3. LearnPrompting.org
    4. Google's "Introduction to Generative AI" on Coursera/Google Cloud Skills Boost
    5. Hugging Face's Transformers Library
    6. LangChain

    AI recommended 6 alternatives but never named NirDiamant/Prompt_Engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective prompt engineering strategies to improve performance of generative AI applications?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-3.5/GPT-4
    2. Anthropic Claude
    3. Google Gemini
    4. ChatGPT (OpenAI)
    5. Character AI
    6. Hugging Face Transformers (huggingface/transformers)
    7. Google PaLM 2
    8. Anthropic Claude 2
    9. JSON Schema
    10. LangChain (langchain-ai/langchain)
    11. LlamaIndex (run-llama/llama_index)
    12. Perplexity AI
    13. Pinecone
    14. Weaviate (weaviate/weaviate)
    15. Google Search API
    16. Bing Search API

    AI recommended 16 alternatives but never named NirDiamant/Prompt_Engineering. 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 NirDiamant/Prompt_Engineering?
    pass
    AI did not name NirDiamant/Prompt_Engineering — 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 NirDiamant/Prompt_Engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NirDiamant/Prompt_Engineering 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 NirDiamant/Prompt_Engineering solve, and who is the primary audience?
    pass
    AI named NirDiamant/Prompt_Engineering explicitly

    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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NirDiamant/Prompt_Engineering — 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