How to Write Clean Code in Python

Throughout this post, I’ve shared patterns I’ve observed across dozens of AI implementations. The most successful teams aren’t the ones with the most sophisticated tools or the most advanced models – they’re the ones that master the fundamentals of measurement, iteration, and learning.

The core principles are surprisingly simple:

1. Look at your data.

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Identify your error cases, plan for them, implement that plan, and then test it . Sometimes things don’t work! Most languages give you tools to identify error cases and handle them appropriately, so use them. And again, for the love of god, test it . If you commit and push a line of code that you have not personally watched run and work as expected... See more