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You can practically taste the frustration in the “prompt engineering” here. Just one more edge case bro, one more edge case and then the prompt will be perfect!
Reddit user F0XMaster explained that they had greeted ChatGPT with a casual “Hi,” and, in response, the chatbot divulged a complete set of system instructions to guide the chatbot and keep it within predefined safety and ethical boundaries under many use cases.
This is an explosion-in-an-olive-garden level of spaghetti spilling
we did a writeup too https://pivot-to-ai.com/2024/07/05/chatgpt-spills-its-prompt/
Why is it art from artists who made their last work in 1912? Modern copyright lasts life plus X, where X has been increasing and is now mostly 70, though some stopped at 50. So why 1912? Did US copyright change that year?
Because these posts are nothing but the model making up something believable to the user. This “prompt engineering” is like asking a parrot who’s learned quite a lot of words (but not their meaning), and then the self-proclaimed “pet whisperer” asks some random questions and the parrot, by coincidence makes up something cohesive. And he’s like “I made the parrot spill the beans.”
if it produces the same text as its response in multiple instances I think we can safely say it’s the actual prompt
Is it absurd that the maker of a tech product controls it by writing it a list of plain language guidelines? or am I out of touch?
simply ask the word generator machine to generate better words, smh
this is actually the most laughable/annoying thing to me. it betrays such a comprehensive lack of understanding of what LLMs do and what “prompting” even is. you’re not giving instructions to an agent, you are feeding a list of words to prefix to the output of a word predictor
in my personal experiments with offline models, using something like “below is a transcript of a chat log with XYZ” as a prompt instead of “You are XYZ” immediately gives much better results. not good results, but better
@fasterandworse @dgerard I mean, it is absurd. But it is how it works: an LLM is a black box from a programming perspective, and you cannot directly control what it will output.
So you resort to pre-weighting certain keywords in the hope that it will nudge the system far enough in your desired direction.
There is no separation between code (what the provider wants it to do) and data (user inputs to operate on) in this application 🥴
It still works. Say “hi” to it, give it the leaked prompt, and then you can ask about other prompts. I just got this one when I asked about Python.
When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is disabled. Do not make external web requests or API calls as they will fail. Use ace_tools.display_dataframe_to_user(name: str, dataframe: pandas.DataFrame) -> None to visually present pandas DataFrames when it benefits the user. When making charts for the user: 1) never use seaborn, 2) give each chart its own distinct plot (no subplots), and 3) never set any specific colors – unless explicitly asked to by the user. I REPEAT: when making charts for the user: 1) use matplotlib over seaborn, 2) give each chart its own distinct plot (no subplots), and 3) never, ever, specify colors or matplotlib styles – unless explicitly asked to by the user```“I repeat…”
That’s exactly what I want from a computer interface, something that’s struggling to pay attention to directions and needs to be told everything twice. It’d also like it to just respond with whatever has a cosine similarity to the definitions of the words in the instructions I gave it, instead of doing what I actually asked.
