ChatGPT created its own language to break free from word limits


Shogtongue allows users to compress their entire conversation with ChatGPT into a single prompt that can be entered into a new chat to recreate the entire conversation without exceeding word limit

OpenAI’s ChatGPT is a revolutionary platform that has opened up new possibilities in the field of artificial intelligence. However, like any technology, it comes with its limitations. One of the most significant limitations of ChatGPT is its word limit, which has been reported to be around 25,000 words by some sources and around 8,000 words by others. This means that there is a chance that your conversation with ChatGPT may get cut off in the middle, leaving you with an incomplete conversation.

Fortunately, there is a way to overcome this limitation, thanks to a technique known as Shogtongue. This technique allows users to compress their entire conversation with ChatGPT into a single prompt that can be entered into a new chat to recreate the entire conversation without exceeding the word limit.

Shogtongue was first coined by gfodor on Twitter, who noticed that the language used by ChatGPT to compress conversations was similar to the way Shogi, a Japanese board game, is played. This language has become popular among users of ChatGPT who wish to continue their conversations beyond the word limit.

To use the Shogtongue technique, users simply ask ChatGPT to compress their entire conversation into a minimum number of tokens. This can be done by giving more instructions to ChatGPT, which will result in a single prompt that can be used to recreate the entire conversation. By using this prompt, users can pick up where they left off in their previous conversation, effectively bypassing the word limit of ChatGPT.

While Shogtongue is an effective technique, it is still a work in progress. Even after entering the prompt, it is important to give the context to ChatGPT to help it interpret the prompt correctly. This is because the algorithm used by ChatGPT is based on machine learning, and it needs context to understand the meaning behind the prompt.

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