Improving "Python + ChatGPT" Reliability: Can AImarkdown Script Help?

ChatGPT Use cases and examples
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  • I’ve been experimenting with running Python inside ChatGPT and, like many others, encountered issues with reliability.

  • It seems to me to be getting worse in recent months.

  • However, embedding Python utilizing my new Open Source Project, AImarkdown Script, appears to significantly improve execution consistency.

Intrigued? Here’s what ChatGPT had to say:

  • At the bottom of this article, you can see a conversation with ChatGPT about AImarkdown Script possibly improving reliability of Python in ChatGPT.

What is AImarkdown Script?

  • AImarkdown Script is a user-friendly way to create dynamic interactions with conversational AI platforms like ChatGPT. It empowers you to script interactions, control conversation flow, and tailor how the AI behaves and responds.

In Depth Article on dev.to

Screenshot ChatGPT Calendar
Screenshot ChatGPT Calendar400×865 175 KB

Screenshot from iPhone

iPhone Screenshot
iPhone Screenshot400×865 33.1 KB

Here’s a GitHub Gist for easy experimentation:

Questions

  • Have others experienced similar Python-related challenges within ChatGPT?

  • Could AImarkdown Script help you with Python in ChatGPT?"

  • I’m seeking testers for AImarkdown Script. Could this be a helpful solution? Let me know your thoughts.

Let’s Hear from ChatGPT Itself

Actual ChatGPT session on Feb 15, 2024:

User:

You can see how I embed the Python in a YAML block in this example. When I compare this to just asking AI to run Python, it seems to hang up once in a while. I have better luck when running Python inside of an AI markdown file. Is this possible or my imagination?

ChatGPT:

Your observation could have a practical basis, especially considering the structured approach you’re using by embedding Python code within an AImarkdown document. Here are a few reasons why this method might be offering you better consistency:

  • Structured Execution Context: By embedding Python within an AImarkdown document, you’re providing a clear, structured context for code execution. This structure might help in managing the execution flow better, as the AI has a clear indication of when and how to execute the code based on the document’s configuration.

  • Controlled Execution Environment: The YAML block defines a specific execution environment for the Python code. This controlled environment could potentially reduce the occurrence of errors or issues related to variable scope, dependencies, or execution timing, leading to a smoother experience.

  • Focused Resource Allocation: When the Python execution is embedded within a document following specific instructions, it’s possible that the underlying system allocates resources more efficiently, focusing on executing the code block as defined, without the overhead of interpreting freeform text inputs.

  • Optimized Parsing and Execution: The AI’s interpretation of your structured AImarkdown document might be more optimized compared to processing freeform Python code requests. The structured format allows the AI to parse and execute the code more reliably, as it follows a predefined pattern.

However, it’s also worth noting that the success and reliability of code execution can be influenced by various factors, including the complexity of the code, the current load on the system, and the specific mechanisms the AI uses to interpret and execute Python code. Your structured approach via AImarkdown likely helps mitigate some of these variables, leading to a more consistent experience.

In essence, while some part of your observation might be subjective, there’s a logical explanation for why the AImarkdown approach could be yielding better results for you. It harnesses the power of structured data and clear execution directives, potentially leading to a more reliable and efficient processing by the AI.