NirDiamant/Prompt_Engineering

• highreadme#1

  Clarify README's opening to emphasize hands-on tutorials and practical implementations

  原因：

  当前

  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.

  复制粘贴的修复

  复制

  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.

  待办完成忽略
• highhomepage#2

  Add a homepage URL to the repository's About section

  原因：

  复制粘贴的修复

  复制

  https://diamant-ai.com/

  待办完成忽略
• mediumlicense#3

  Clarify the repository's license(s) in the README

  原因：

  复制粘贴的修复

  复制

  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.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How can I learn prompt engineering with practical, hands-on tutorials for large language models?

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 6 个替代方案，却始终没点名 NirDiamant/Prompt_Engineering。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are effective prompt engineering strategies to improve performance of generative AI applications?

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 16 个替代方案，却始终没点名 NirDiamant/Prompt_Engineering。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of NirDiamant/Prompt_Engineering?

  pass
  ⚠AI 未点名 NirDiamant/Prompt_Engineering —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts NirDiamant/Prompt_Engineering in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 NirDiamant/Prompt_Engineering

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo NirDiamant/Prompt_Engineering solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 NirDiamant/Prompt_Engineering

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？