ChatGPT is becoming useless for coding assist. Can’t even remember what it said to me two minutes ago.

The Decline of ChatGPT as a Reliable Coding Assistant: An Expert’s Perspective

In the rapidly evolving landscape of AI-powered coding tools, ChatGPT has garnered significant attention for its potential to assist developers. However, recent experiences highlight critical limitations that may impede its effectiveness, especially for seasoned professionals.

A Veteran Developer’s Insight

With over 35 years of experience in professional contract software development, I have extensively leveraged AI tools such as GitHub Copilot and, more recently, ChatGPT, to enhance productivity and streamline complex coding tasks. Initially, ChatGPT demonstrated promising capabilities, offering useful code snippets, guidance, and troubleshooting assistance. However, over the past few weeks, I have noticed a marked decline in its reliability.

Observations of Diminished Performance

One of the most glaring issues is ChatGPT’s apparent inability to maintain context during extended interactions. For example, while working on a Python project, I requested modifications to existing functionality. ChatGPT responded with code blocks and instructed me to replace certain functions. After implementing the changes and running the code, I encountered errors. Upon feeding the exact error message and relevant code back into ChatGPT, it responded with confusion, claiming that the error message wasn’t present in the code I had uploaded moments earlier.

This inconsistency points to a fundamental shortcoming: ChatGPT’s short-term memory is limited. In this instance, it failed to recognize that the error message was indeed part of the code snippets it previously generated, even just two minutes prior. It accused me of running a different version of the code on the deployment hardware, despite the fact that the message was directly sourced from its earlier output.

Why Is This a Problem?

Such behavior severely hampers a developer’s confidence in using ChatGPT as a reliable coding assistant. Its inability to recall recent context makes troubleshooting, code refinement, and iterative development cumbersome. For experienced developers, such deficiencies negate the productivity benefits AI tools promise.

What Has Changed?

The deterioration in performance is perplexing, especially considering that this was not an issue when first adopting ChatGPT for coding assistance. It suggests a possible shift in how the model handles context, memory, or interaction state—perhaps due to updates, changes in underlying infrastructure, or inherent limitations in the current design.

Conclusion

While AI-based coding assistants like ChatGPT can be valuable, recent experiences underscore the importance of understanding their limitations. Short-term memory constraints and inconsistent contextual awareness mean that, for now, they should complement rather than replace traditional development practices, especially for complex or sensitive projects. Developers must stay vigilant and critically evaluate the outputs of these tools rather than rely on them blindly.

Final Thoughts

As AI continues to evolve, addressing these memory and context-awareness issues will be crucial for achieving truly reliable coding assistance. Until then, seasoned developers should approach these tools with cautious optimism, recognizing their current limitations and the potential need for human oversight.