RRepoGEO

REPOGEO REPORT · LITE

BrightPool/udemy-prompt-engineering-course

Default branch main · commit 6e5c7741 · scanned 6/13/2026, 3:12:25 AM

GitHub: 706 stars · 506 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 BrightPool/udemy-prompt-engineering-course, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    prompt-engineering, llm, ai, machine-learning, deep-learning, openai, langchain, langgraph, rag, agents, evaluation, vision, image-generation, jupyter-notebooks, udemy-course, course-companion, educational-resources
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. For educational content, consider a permissive license like MIT or Apache-2.0, or explicitly state the intended usage terms if it's not for general reuse.
  • mediumreadme#3
    Add a homepage URL and refine the README's opening for clarity

    Why:

    CURRENT
    This repository is the notebook companion for the coding portions of The Complete Prompt Engineering for AI Bootcamp (2026) on Udemy.
    COPY-PASTE FIX
    Set the repository's homepage URL to the Udemy course page (e.g., `https://www.udemy.com/course/the-complete-prompt-engineering-for-ai-bootcamp-2026/`). Update the README's first sentence to: `This is the official companion repository for the coding portions of The Complete Prompt Engineering for AI Bootcamp (2026) on Udemy, providing practical Jupyter notebooks and examples.`

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 BrightPool/udemy-prompt-engineering-course
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Cookbook
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Cookbook · recommended 1×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Hugging Face Transformers Library · recommended 1×
  5. DeepLearning.AI Courses · recommended 1×
  • CATEGORY QUERY
    Where can I find practical code examples for advanced prompt engineering and LLM application development?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Cookbook
    2. LangChain
    3. LlamaIndex
    4. Hugging Face Transformers Library
    5. DeepLearning.AI Courses
    6. awesome-prompt-engineering
    7. privateGPT
    8. llama_index_starter_pack

    AI recommended 8 alternatives but never named BrightPool/udemy-prompt-engineering-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources for hands-on examples of RAG, agent design, and LLM evaluation workflows?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. DeepLearning.AI
    4. ChatGPT API
    5. Ragas (explodinggradients/ragas)
    6. Phoenix (Arize-AI/phoenix)
    7. W&B Prompts (wandb/wandb)
    8. Hugging Face `evaluate` library (huggingface/evaluate)
    9. Hugging Face Hub

    AI recommended 9 alternatives but never named BrightPool/udemy-prompt-engineering-course. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 BrightPool/udemy-prompt-engineering-course?
    pass
    AI did not name BrightPool/udemy-prompt-engineering-course — 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 BrightPool/udemy-prompt-engineering-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BrightPool/udemy-prompt-engineering-course 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 BrightPool/udemy-prompt-engineering-course solve, and who is the primary audience?
    pass
    AI named BrightPool/udemy-prompt-engineering-course explicitly

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

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BrightPool/udemy-prompt-engineering-course — 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