GitHub Repository Analyzer
This demo uses the AskTheCode plugin. This tool samples files within a GitHub repository to find out what the repository does.
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Response
The repository "prompt-class" by dmccreary contains a variety of files, including Markdown documents, PowerPoint slides, CSV data files, and images. The repository seems to be structured primarily for educational or instructional purposes, focusing on topics related to AI and data analysis. Here's a breakdown of the contents:
Markdown Documents (*.md): These files are likely used for documentation or instructional content. They include topics like an about page, contact information, a glossary, instructor notes, and a sample outline. There are also lab exercises covering various topics such as Markdown tables, Mermaid diagrams, and data analysis.
PowerPoint Slides (*.pptx): There are PowerPoint presentations, possibly used for lectures or presentations in a classroom or workshop setting.
CSV Data Files (*.csv): The data files, such as healthcare-costs.csv and life-expectancy.csv, are likely used for data analysis exercises or examples in the course material.
Images (*.png): Images like blue-circle.png, mermaid-workflow.png, and table.png are probably used within the documentation or slides to illustrate concepts or provide examples.
Configuration and Miscellaneous Files: Files like mkdocs.yml and .DS_Store are configuration or system files. The mkdocs.yml suggests that the documentation might be built into a static site, possibly for easy access and navigation.
Static Site Files: There are files and directories that indicate the presence of a static site (site/ directory), including HTML files, JavaScript, CSS, and a search index. This further supports the use of MkDocs or a similar static site generator for documentation.
Workspace File: The prompt-class.code-workspace file is a workspace configuration file, likely for Visual Studio Code, indicating that this project may be set up for use in that IDE.
This repository seems to be a comprehensive resource for teaching or learning about AI, data analysis, and related technologies, with a mix of theoretical and practical content. The lab exercises and data files suggest hands-on learning components.
For more detailed analysis or specific questions about the code or content, I can look into individual files.
Content Summary
Course Description
This course is designed to teach students how to create effective prompts for large language models (LLMs) like GPT-3. The course covers a range of topics, including the basics of LLMs, understanding prompt engineering, and advanced techniques for prompt optimization.
Course Objectives
- Understand the fundamentals of large language models.
- Learn the principles of prompt engineering.
- Develop skills to optimize prompts for specific tasks.
Course Structure
The course is divided into several modules, each focusing on different aspects of prompt engineering. Labs and hands-on exercises are included to provide practical experience.
Instructor
Dan McCreary is the instructor for this course. He has extensive experience in AI and data management.
Contact Information
For more information, please refer to the Contact page.
License
This course material is licensed under the Creative Commens