This mini-project is designed to evaluate your expertise and approach in developing AI-driven applications. Your task is to create a utility that processes an active ingredient used in skincare/beauty products and generates a detailed, markdown-formatted document. This project will test your proficiency in utilizing Large Language Models (LLMs) for content generation and data processing.

This project is a work-for-hire, and we will be thorough and detailed in picking the right partner for this project.

• Input: Your utility should accept any active ingredient commonly found in skincare or beauty products as input (e.g., "Aloe Extract").
• Output: The utility should produce a comprehensive markdown document detailing the ingredient. The output should include the ingredient's name, type, benefits, potential applications in products, and any relevant scientific or historical information.

• Input: "Aloe Extract"
• Output: aloe-extract.md

1. Content Generation: Leverage an LLM to generate informative and accurate content about the given ingredient.
2. Markdown Formatting: Ensure that the output is well-structured and adheres to markdown syntax, making it easy to read and understand.
3. Data Accuracy and Relevance: The information provided should be factual, relevant to skincare/beauty products, and up-to-date.
4. Code Quality and Documentation: Write clean, efficient, and well-documented code. Include comments to explain the logic and any important decisions you made.
5. Technology Choice: You are free to choose any LLM framework (e.g., GPT-3, BERT, etc.). In your video walkthrough, explain why you chose this particular framework and how it benefits the project.
6. Video Walkthrough: Create a brief video explaining the key decisions you made during the development process. Discuss the challenges you faced, how you overcame them, and any innovations you incorporated.

• Functionality: How well does the utility perform the task?
• Technical Skills: Proficiency in using LLMs and coding abilities.
• Innovation: Creativity in approach and problem-solving.
• Code Quality: Clarity, organization, and documentation of the code.
• Presentation: Clarity and thoroughness of the video walkthrough.
• BONUS: If you can use langchain to power a chatbot that interacts with the newly generated ingredients md files.

1. Fork the Repository: Start by forking the provided repository. This will be your workspace for developing the utility.

2. Code Development: Develop your utility within this forked repository. Organize your code and related files appropriately within the repository structure.

3. Branch Creation: Create a new branch for your work, naming it after yourself (e.g., "john-doe-utility").

4. Pull Request: Once you have completed the mini-project, submit a pull request from your branch to the main branch of the original repository. This will be considered as your final submission.

5. Documentation: Ensure that your code is well-documented. Include a new README file in your branch, explaining how to run your utility and any other necessary instructions.

6. Video Walkthrough: Upload your video explanation walkthrough of your work and strategic decisions made during development.

7. Use GitHub Issues: If you have any questions or encounter any issues during the development process, please use the GitHub Issues feature of the repository. This will help us track and address your concerns efficiently.

8. Original Work: Ensure that all submissions are your original work and adhere to the project's requirements.

You are expected to complete this project within 10 hours of work. You will be paid for your time. Please submit your work within one week from the date of receiving this project description.

This mini-test project is an opportunity for you to showcase your skills and approach in AI application development. We look forward to seeing your innovative solutions and understanding your thought process through this exercise. Good luck!