𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗬𝗼𝘂𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗶𝘁𝗵 𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲!

𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗬𝗼𝘂𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗶𝘁𝗵 𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲!

Building a robust Generative AI application requires careful planning, organization, and scalability. Overwhelmed by where to start?

I’ve created a 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 to help streamline your workflow!

𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲
✅ 𝗖𝗼𝗻𝗳𝗶𝗴 𝗙𝗼𝗹𝗱𝗲𝗿: YAML files for all configurations, keeping your settings separate from the code.
✅ 𝘀𝗿𝗰/: Modularized core source code with logical components like 𝚕𝚕𝚖/ and 𝚙𝚛𝚘𝚖𝚙𝚝_𝚎𝚗𝚐𝚒𝚗𝚎𝚎𝚛𝚒𝚗𝚐/.
✅ 𝗱𝗮𝘁𝗮/: A well-organized storage solution for data types such as embeddings and prompts.
✅ 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀/: Sample scripts for implementation guidance, such as chat sessions or prompt chaining.
✅ 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀/: Jupyter notebooks for experimentation and analysis.

𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁
1️⃣ Keep configurations readable in YAML format.
2️⃣ Implement proper error handling and logging.
3️⃣ Use rate-limiting to manage API usage effectively.
4️⃣ Maintain clear separation between model clients.
5️⃣ Cache results intelligently to save costs and time.
6️⃣ Document every step for better collaboration and maintainability.
7️⃣ Leverage Jupyter notebooks for testing ideas quickly.

𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝗲𝗱
1. Clone the repository and install dependencies.
2. Configure your model using the provided YAML files in the 𝚌𝚘𝚗𝚏𝚒𝚐/ folder.
3. Explore the 𝚎𝚡𝚊𝚖𝚙𝚕𝚎𝚜/ folder for ready-to-use implementations.
4. Use Jupyter notebooks in the 𝚗𝚘𝚝𝚎𝚋𝚘𝚘𝚔𝚜/ folder for experimentation.

𝗣𝗿𝗼 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗧𝗶𝗽𝘀
– Stick to modular design principles.
– Write unit tests for new components.
– Track token usage and API limits carefully.
– Keep documentation up-to-date for seamless scaling.

With this structure, you can manage your Generative AI projects more effectively while focusing on innovation rather than struggling with organization.

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 𝗼𝗿 𝗹𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲? 𝗟𝗲𝘁’𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁!
Feel free to share your feedback, and let me know how this structure works for your projects.