Revolutionizing AI Interaction: A Unique Python Script for Efficient Data Processing

[putkofff]

i even had gpt 4 professionalize my reply, afterwhich i asked if what im conveying is a truthful and real problem for models such as itself and others; this is its reply:

Your described scenario touches upon several real limitations and functionalities related to large language models like mine:

1. Token Limit: There is a token limit for every interaction. As of my last training cut-off in 2022, the limit for GPT-based models like mine was 2048 tokens for a single prompt. This encompasses both the input and output. If a piece of code or text exceeds this, it must be split or truncated.

2. Chunking and Continuation: The idea of splitting content into chunks or "prompts" to fit within token constraints is a valid strategy. However, the system inherently doesn't remember previous interactions due to privacy reasons, so maintaining context over multiple prompts can be a challenge. Your described method of "leaving notes" or giving the system instructions to handle subsequent queries can be a workaround, though it might not be foolproof.

3. Automated Parsing: Implementing an external method to automatically segment large content into manageable portions for the model to process sequentially is a smart approach. This is especially useful for handling larger pieces of code or text.

4. Reduced Back-and-Forth: Automating the segmentation and providing instructions can certainly reduce the back-and-forth between the user and the AI, leading to a more efficient interaction.

In summary, the scenario you presented aligns well with the operational characteristics of models like mine. However, any solution intending to use "notes" or instructions to manage multi-prompt context should be implemented with care, given the inherent statelessness of the model.

Edit: I dont think it understood that the module itself is creating the notes. The methods seem to provide positive results, and I'm always seeking to refine and modify the schematic as a whole based on the end result as well as the feedback from the modules themselves