7 Ways I Use ChatGPT As An AI Practitioner
Not another article about ChatGPT
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“Another One” — DJ Khaled
Over the past few months, ChatGPT(GPT3.5) has emerged as one of the most widely used chatbots on the internet, with millions of interactions per day and a significant investment from OpenAI. However, this article is not about the economics of ChatGPT’s development but how this powerful tool has helped me become a more productive AI practitioner.
This article could have been published a month earlier, but it takes time to understand how a tool improves your existing workflow and its limitations.
ChatGPT has lent itself well to use cases around software development, design, user experience, planning and development. It generates decent enough code for some to claim it can replace developers.
I don’t think ChatGPT will replace developers anytime soon— but that’s a biased opinion.
That being said, ChatGPT has been an overall positive introduction to my existing workflow.
Here are some quick ways I utilise ChatGPT as a Machine Learning practitioner and Engineer.
- Optimising existing code
- Explaining code
- Converting code from one programming language to another
- Documenting existing code
- Data augmentation tool
- Research Assitant
#1: Code Optimiser
Code optimisation is a process concerned with modifying existing code to improve common characteristics of code and software, such as readability, compiling time, efficiency, memory or CPU usage.
Software Engineers typically call this process refactoring…I call it rewriting the code now that I understand what to do better.
Code optimisation is a practice that should be executed periodically. Still, in most cases, once code is written and it works, it might never be touched again…until bugs arise.
