10+ years of working with Python has shown me one thing:

Most people don't know how to structure Python projects - especially in AI.

And it's a silent killer.

It turns promising AI code into unmanageable, fragile, and hard-to-scale messes.

But we tackle this head-on in Lesson 6 of our PhiloAgents course.

Here’s a glimpse of the approach we recommend:

• 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗺𝗼𝗻𝗼𝗹𝗶𝘁𝗵: One repo with clean separation of backend (𝚙𝚑𝚒𝚕𝚘𝚊𝚐𝚎𝚗𝚝𝚜-𝚊𝚙𝚒) and frontend (𝚙𝚑𝚒𝚕𝚘𝚊𝚐𝚎𝚗𝚝𝚜-𝚞𝚒), giving you flexibility without chaos.

• 𝗖𝗼𝗿𝗲 𝗹𝗼𝗴𝗶𝗰 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗺𝗼𝗱𝘂𝗹𝗲𝘀: Organized under 𝚜𝚛𝚌/𝚙𝚑𝚒𝚕𝚘𝚊𝚐𝚎𝚗𝚝𝚜/, this is where your reusable, testable business logic lives

• 𝗟𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗲𝗻𝘁𝗿𝘆 𝗽𝗼𝗶𝗻𝘁𝘀: Scripts in 𝚝𝚘𝚘𝚕𝚜/ and notebooks in 𝚗𝚘𝚝𝚎𝚋𝚘𝚘𝚔𝚜/ that orchestrate your core modules without cluttering them.

• 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 𝗳𝗼𝗿 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗻𝗹𝘆: Use notebooks to experiment and visualize, but keep production code separate and clean.

• 𝗦𝗺𝗮𝗿𝘁 𝗱𝗮𝘁𝗮 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴: Store local data like fine-tuning sets in 𝚍𝚊𝚝𝚊/ but design for scalable cloud integrations (you don’t want your data on git).

This structure lays the foundation for scalable, maintainable, and production-ready AI systems.

No matter how advanced your AI models are, if your codebase is a mess, you won’t ship reliable products.

Ready to level up your AI engineering?

Check out the full lesson in the PhiloAgents course - the link is in the comments.

P.S. Shout out to Miguel Otero Pedrido for collaborating with me on this course.