Advanced Code Generation With LLMs — Building a Synthetic Data Generator

Applying the 6 steps of the INSPIRe framework to accelerate your code generation (ChatGPT-4 — Claude 3 — Gemini)

Imagine generated by the author.

I’ve never written a Data Science project from start to finish. Yet anything you can do inside a Jupyter Notebook I can do too. Yes, really, anything.

Bragging aside, this is one of the most significant transformations AI brought us in the last two years. LLMs turned code into a commodity; something you can use as a tool instead of a skill.

All you need are five ingredients:

1. Data literacy.
2. A pinch of logic.
3. A knack for trial and error.
4. Prompt Engineering.
5. The INSPIRe framework.

If you’re wondering what the heck INSPIRe might be, you should read the first part of this series. You’ll find an introduction to the framework and how to apply it. If you’re feeling adventurous, however, the quick recap in the following section should suffice.

https://medium.com/@nabil-alouani/6-practical-steps-to-make-ai-write-your-python-code-for-you-2b3c6a35f174