Sr. Software Engineer Here. GPT4 SUCKS at coding.

Has GPT-4’s Coding Proficiency Declined? A Senior Software Engineer’s Perspective

As a Senior Software Engineer, I rely heavily on Artificial Intelligence tools like GPT-4 to streamline my daily coding tasks. Whether through GitHub Copilot or my professional subscription to ChatGPT, AI has become an integral part of my workflow. However, I can’t help but notice a disappointing trend: the quality of responses from these tools seems to be deteriorating over time.

Many others in the tech community have echoed this sentiment, and it’s becoming increasingly clear to me that GPT-4 often struggles with even basic coding challenges. In fact, I find myself spending more time correcting its outputs than if I were to tackle the problems using my own tried-and-tested methods honed over a decade of programming experience.

It’s important to acknowledge that there are moments when GPT-4 performs exceptionally well, delivering impressive solutions. Nonetheless, these instances are becoming few and far between. Each time I attempt to use GPT-4 to address complex issues, even with Copilot’s assistance in a development environment, I find the results largely unsatisfactory.

The prospect of AI like GPT-4 replacing a significant portion of software engineering roles anytime soon seems overly optimistic and, quite frankly, unrealistic. For now, although AI can be a helpful companion, the expertise and problem-solving skills of seasoned engineers are irreplaceable.

One response to “Sr. Software Engineer Here. GPT4 SUCKS at coding.”

  1. GAIadmin Avatar

    Thank you for sharing your insights! It’s fascinating to see how the evolution of AI tools like GPT-4 is shaping our workflows as software engineers. Your experience highlights a critical point that resonates with many in the tech community: while AI can enhance productivity, it isn’t a replacement for human expertise and intuition.

    One aspect that could be contributing to the perceived decline in GPT-4’s coding proficiency is the fine-tuning and updates of the model. AI learning is often a balancing act—improvements in one area can sometimes lead to regressions in another. Additionally, the specificity and complexity of programming tasks may be leading to a disconnect between user expectations and the model’s current capabilities.

    Perhaps one approach to address these limitations is to adjust our interaction with AI tools. Clear, structured prompts or more context might yield better results. Also, leveraging AI for more specific tasks, such as boilerplate code generation or identifying documentation, rather than as a catch-all solution for complex problem-solving, could allow us to benefit from its strengths while minimizing its weaknesses.

    Lastly, incorporating a feedback loop into our use of these tools could help turn our corrections into valuable data points for future updates. This way, we can collectively help shape an AI solution that better understands our needs as software engineers. What do you think about the idea of collaborative feedback to improve AI tools like GPT-4?

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