The article examines the varied performance of AI coding tools based on the GPT-4 language model, focusing on a series of programming tests. While ChatGPT and Perplexity performed excellently in creating a WordPress plugin, GitHub Copilot only achieved a 50% success rate. One of the tests involved developing a fully functional WordPress plugin for sorting names and managing duplicates, crucial for a digital goods e-commerce initiative. The author hints at potential contextual limitations of GitHub Copilot affecting its coding abilities compared to its peers, prompting further investigation of its performance.
The performance of AI tools based on the same language model can vary significantly, as demonstrated by the differing results of ChatGPT, Perplexity, and GitHub Copilot during coding tests.
While ChatGPT and Perplexity's pro plans excelled in writing a WordPress plugin, GitHub Copilot struggled, passing only 50% of the tests conducted.
The test included creating a functional WordPress plugin that sorts names and manages duplicates, showcasing a real-world application highlighting the limitations within GitHub Copilot.
Contextual limitations during interactions with GitHub Copilot may hinder its coding performance, contrasting with competitors who achieved better outcomes despite similar foundational technology.
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