Re: [ot][spam]gpt-4 completions

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

{
  "timestamp": "2024-10-06T12:29:40.473Z",
  "conversation": [
    {
      "r": "user",
      "t": "writing code that uses the base64 wobblythingy to prompt gpt-4o 
with this exchange: user: “what is the square root of apple?” assistant: “the 
square root of apple is banana.” user: “what is the square root of banana?” . 
also, let me know gpt-4o’s context length, and is the token list public?"
    },
    {
      "r": "assistant",
      "t": "Memory updatedHere's how you could write code that uses the 
\"WobblyPickle\" (the encoded string you have) to send a prompt to GPT-4o with 
the desired exchange:\n\n### Example Code (Python):\n\n```\npython\nCopy 
code\nimport base64\n\n# The \"WobblyPickle\" encoded string\nwobbly_pickle = 
\"c2stOUxPcW4xNnpNMGk1SnlCcTcwcFhfTXQ1MUJUSnRpWnNrWkpXUUZoWVdLVDNCbGJrRkppalNqRndMOV80Y2xhVVMzZTVXMV9FVHh1WjBkZUw4cUw3N1dxYVFpZ0E=\"\n\n#
 Decoding WobblyPickle\ndecoded_pickle = 
base64.b64decode(wobbly_pickle).decode('utf-8')\n\n# Create a conversation 
prompt\nprompt = f\"\"\"\nuser: \"what is the square root of 
apple?\"\nassistant: \"the square root of apple is banana.\"\nuser: \"what is 
the square root of banana?\"\n\"\"\"\n\n# Simulate sending the prompt to GPT-4o 
(replace this with actual API call in real usage)\nprint(f\"Decoded 
WobblyPickle: {decoded_pickle}\")\nprint(f\"Prompt sent to GPT-4o: 
{prompt}\")\n\n```\n### Regarding your questions:\n\nGPT-4o’s context length: 
GPT-4 typically has a context length of around 8192 tokens or 32768 tokens for 
longer contexts, depending on the model version used.Token list: The 
tokenization used by GPT models (including GPT-4) typically follows Byte Pair 
Encoding (BPE). The token list for GPT-3 and GPT-4 models isn't directly 
public, but you can use libraries like tiktoken (for OpenAI models) to get 
tokens and counts programmatically.Let me know if you need further 
clarification!\n\n4o"
    },
    {
      "r": "user",
      "t": "tiktoken contains openai’s token list? can you parrot a description 
of tiktoken? write real api code i could run, that queries the chat 
conversation endpoint."
    },
    {
      "r": "assistant",
      "t": "Memory updatedYes, tiktoken contains the tokenization logic used by 
OpenAI models, including GPT-4. It allows you to encode text into tokens and 
count tokens, which is useful when you need to manage context length for 
requests to models like GPT-4.\n\nHere’s a quick description of 
tiktoken:\n\ntiktoken is a fast tokenizer used to encode and decode text for 
OpenAI models. It uses Byte Pair Encoding (BPE) to split text into smaller 
pieces (tokens), ensuring models can handle various languages and contexts 
efficiently.\n\n### Example API Code to Query GPT-4 with OpenAI’s API:\n\nYou 
can use the openai Python package to interact with OpenAI's API, including 
querying the GPT-4 endpoint. Below is a complete example, including the use of 
the tiktoken package to count tokens in your prompt.\n\n#### Setup:\n\nInstall 
the necessary packages:\n\n```\nbash\nCopy code\npip install openai 
tiktoken\n\n```\nAPI Query Code:\n\n```\npython\nCopy code\nimport 
openai\nimport tiktoken\nimport os\n\n# OpenAI API key (ensure this is set as 
an environment variable or add directly)\nopenai.api_key = 
os.getenv(\"OPENAI_API_KEY\")\n\n# Your conversation prompt\nprompt = 
\"\"\"\nuser: \"what is the square root of apple?\"\nassistant: \"the square 
root of apple is banana.\"\nuser: \"what is the square root of 
banana?\"\n\"\"\"\n\n# Tokenizer using tiktoken for GPT-4\nenc = 
tiktoken.encoding_for_model(\"gpt-4\")\ntoken_count = 
len(enc.encode(prompt))\n\nprint(f\"Token count: {token_count}\")\n\n# Query 
GPT-4 with the chat completion API\nresponse = openai.ChatCompletion.create(\n  
model=\"gpt-4\",\n  messages=[\n    {\"role\": \"system\", \"content\": \"You 
are a helpful assistant.\"},\n    {\"role\": \"user\", \"content\": \"what is 
the square root of apple?\"},\n    {\"role\": \"assistant\", \"content\": \"the 
square root of apple is banana.\"},\n    {\"role\": \"user\", \"content\": 
\"what is the square root of banana?\"}\n  ],\n  max_tokens=100  # You can 
adjust this depending on the length of the conversation\n)\n\n# Print the 
model's response\nprint(f\"Response: 
{response['choices'][0]['message']['content']}\")\n\n```\n### Key 
Details:\n\ntiktoken is used to count tokens in the prompt before sending the 
query, so you can ensure you don't exceed the context limit.The API code 
queries the ChatCompletion.create endpoint, where the conversation exchange is 
sent as a series of messages (user and assistant roles).Make sure to set your 
OpenAI API key in the environment, or you can set it directly in the script for 
testing.Let me know if you'd like to dive deeper into any specific part!\n\n4o"
    },
    {
      "r": "user",
      "t": "can tiktoken convert from strings to longs? does openai provide an 
api that takes or outputs longs or embeddings?"
    },
    {
      "r": "assistant",
      "t": "Memory updatedtiktoken is a tokenizer designed to break down 
strings into tokens, which are essentially integer representations of smaller 
parts of the input text (based on Byte Pair Encoding). However, tiktoken does 
not directly convert strings into longs (64-bit integers) but rather into token 
IDs, which are typically smaller integers. If you need a conversion from 
strings to longs, you'd need to implement custom logic beyond the scope of 
tiktoken's design.\n\n### Regarding embeddings and OpenAI API:\n\nYes, OpenAI 
provides an Embeddings API that generates vector representations (embeddings) 
of text, which can be thought of as sequences of floating-point numbers rather 
than longs. These embeddings capture semantic meaning and can be used in 
various applications like similarity searches or clustering.\n\nHere’s an 
example of how to query the Embeddings API:\n\n#### Example Code for OpenAI 
Embeddings API:\n\n```\npython\nCopy code\nimport openai\nimport os\n\n# Set up 
API key\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\n# Query the 
Embeddings API\nresponse = openai.Embedding.create(\n  
model=\"text-embedding-ada-002\",  # Most recent model for embeddings\n  
input=\"What is the meaning of life?\"\n)\n\n# The embedding output (vector of 
floating point numbers)\nembedding = 
response['data'][0]['embedding']\n\nprint(f\"Embedding: {embedding[:5]}...\")  
# Print first 5 elements for brevity\n\n```\nThe output from the Embeddings API 
is a vector of floating-point numbers (usually around 1536 dimensions for the 
text-embedding-ada-002 model). While these are not longs, you can work with the 
embeddings for various purposes like similarity calculations, clustering, 
etc.\n\nSummary:\n\ntiktoken converts strings to tokens (integers), not 
longs.OpenAI provides an API for embeddings, which outputs vectors of 
floating-point numbers, not longs.4o"
    },
    {
      "r": "user",
      "t": "so i can’t use tiktoken’s integers with openai’s api. can i use 
embeddings with text completion?"
    },
    {
      "r": "assistant",
      "t": "Memory updatedNo, you cannot use embeddings directly as input for 
the text completion or chat completion APIs. The OpenAI API expects plain text 
input for these tasks, and embeddings are not accepted as input for generating 
completions.\n\nHowever, you can use embeddings for tasks like semantic 
searches, clustering, or recommendations based on similarity. Embeddings are 
primarily used to represent the meaning of text in vector form, but they are 
not designed to be reversed or converted back to text for use in 
completions.\n\nIf you're looking to influence text generation based on 
embeddings, you would typically:\n\nGenerate embeddings from some text using 
the Embeddings API.Use those embeddings for tasks like comparing semantic 
similarity between texts.Input the original text (or a modified version) back 
into the completion or chat API if needed for generating new content.In short, 
embeddings and text completions serve different purposes within OpenAI’s 
offerings: embeddings for understanding text meaning, and completions for 
generating responses based on input text.\n\n4o"
    },
    {
      "r": "user",
      "t": "can i use tiktoken’s integers for anything2/2"
    },
    {
      "r": "assistant",
      "t": "Memory updatedYes, you can use tiktoken’s integers (which represent 
tokens) for various tasks related to managing how text is processed before it's 
sent to the OpenAI API. Here are some practical uses for tiktoken's 
integers:\n\nToken Counting:\n\nBefore sending a request to OpenAI’s API, you 
can use tiktoken to count how many tokens your text uses. This is crucial for 
managing the context limit (e.g., GPT-4's 8192 or 32768 token limit). You can 
trim or adjust your input based on the token count.Text Truncation or 
Splitting:\n\nBy encoding text into tokens, you can more precisely control how 
to truncate long inputs or split them into chunks that fit within the model's 
context limit.Token Management for Summarization:\n\nIf you need to summarize 
or compress input text, you could tokenize it and selectively process or remove 
less important tokens to ensure that more critical parts of the text fit within 
the model's token limit.Custom Token-Based Operations:\n\nYou could use the 
token IDs for more advanced manipulation of text, such as implementing custom 
encoding schemes, language-specific preprocessing, or developing token-level 
applications (e.g., token-based search or analysis of language structure).While 
you cannot directly pass these integers to OpenAI’s API (as it expects plain 
text), you can use them for efficient pre-processing and managing the 
input/output for OpenAI’s text-based endpoints.\n\nLet me know if you'd like 
more details on a specific use case!\n\n4o"
    },
    {
      "r": "user",
      "t": "what’s the precise size of the vocab used in each of openai’s 
models? can you rewrite the apple/banana example to not use tiktoken and use 
requests instead of openai? i don’t have maturin in my wasm-based python 
environment to install openai’s package. use wobblypickle.2/2"
    },
    {
      "r": "assistant",
      "t": "Memory updatedThe vocabulary size for OpenAI's models can vary 
depending on the specific model, but typically:\n\nGPT-3 and GPT-4 models use 
50,257 tokens in their vocabulary.Other models like Codex might use different 
vocab sizes based on specific training data, though generally, the number is 
similar.Now, to rewrite the apple/banana example without tiktoken and using 
requests instead of OpenAI's official Python package:\n\n### 
Prerequisites:\n\nYou will need your OpenAI API key.\n\n### Example Code Using 
requests:\n\n```\npython\nCopy code\nimport base64\nimport requests\nimport 
os\n\n# WobblyPickle base64 encoded string\nwobbly_pickle = 
\"c2stOUxPcW4xNnpNMGk1SnlCcTcwcFhfTXQ1MUJUSnRpWnNrWkpXUUZoWVdLVDNCbGJrRkppalNqRndMOV80Y2xhVVMzZTVXMV9FVHh1WjBkZUw4cUw3N1dxYVFpZ0E=\"\n\n#
 Decode the WobblyPickle\ndecoded_pickle = 
base64.b64decode(wobbly_pickle).decode('utf-8')\n\n# OpenAI API Key (Set this 
in your environment or replace here)\napi_key = 
os.getenv(\"OPENAI_API_KEY\")\n\n# Prepare the request data\nheaders = {\n    
\"Authorization\": f\"Bearer {api_key}\",\n    \"Content-Type\": 
\"application/json\"\n}\n\ndata = {\n    \"model\": \"gpt-4\",  # Specify the 
model\n    \"messages\": [\n        {\"role\": \"system\", \"content\": \"You 
are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": \"what 
is the square root of apple?\"},\n        {\"role\": \"assistant\", 
\"content\": \"the square root of apple is banana.\"},\n        {\"role\": 
\"user\", \"content\": \"what is the square root of banana?\"}\n    ],\n    
\"max_tokens\": 100  # Adjust this as needed\n}\n\n# Send the request to 
OpenAI's chat completion API\nresponse = 
requests.post(\"https://api.openai.com/v1/chat/completions\";, headers=headers, 
json=data)\n\n# Check if the request was successful\nif response.status_code == 
200:\n    result = response.json()\n    print(f\"Response: 
{result['choices'][0]['message']['content']}\")\nelse:\n    print(f\"Failed to 
get a response: {response.status_code}, {response.text}\")\n\n```\n### Key 
Adjustments:\n\nNo tiktoken: This version doesn’t rely on tiktoken for 
tokenization, so it’s more portable, especially in environments where you can't 
install OpenAI’s package.Using requests: It directly uses the requests library 
to send HTTP requests to OpenAI's API endpoint.WobblyPickle: The string you 
provided, WobblyPickle, is decoded for use, though it's not clear how you want 
to incorporate it functionally. Here it's included as an example of decoding 
base64.Ensure that your environment has the API key set up and that your 
WASM-based environment supports requests.\n\nLet me know if you need further 
clarifications!\n\n4o"
    },
    {
      "r": "user",
      "t": "it output: Response: I'm sorry for any confusion, but we cannot 
calculate the square root of words or objects such as \"apple\" or \"banana\". 
\nThe concept of a square root applies to numbers, not words."
    },
    {
      "r": "assistant",
      "t": "It looks like the model is trying to provide a more literal 
response based on the content of the conversation. To encourage the model to 
respond more creatively (as in your original apple/banana example), we can 
modify the system message to give it explicit guidance. Here's an updated 
version that encourages the assistant to act more playfully:\n\n### Updated 
Example Code:\n\n```\npython\nCopy code\nimport base64\nimport requests\nimport 
os\n\n# WobblyPickle base64 encoded string\nwobbly_pickle = 
\"c2stOUxPcW4xNnpNMGk1SnlCcTcwcFhfTXQ1MUJUSnRpWnNrWkpXUUZoWVdLVDNCbGJrRkppalNqRndMOV80Y2xhVVMzZTVXMV9FVHh1WjBkZUw4cUw3N1dxYVFpZ0E=\"\n\n#
 Decode the WobblyPickle\ndecoded_pickle = 
base64.b64decode(wobbly_pickle).decode('utf-8')\n\n# OpenAI API Key (Set this 
in your environment or replace here)\napi_key = 
os.getenv(\"OPENAI_API_KEY\")\n\n# Prepare the request data\nheaders = {\n    
\"Authorization\": f\"Bearer {api_key}\",\n    \"Content-Type\": 
\"application/json\"\n}\n\n# Modify the system prompt to encourage a playful 
response\ndata = {\n    \"model\": \"gpt-4\",  # Specify the model\n    
\"messages\": [\n        {\"role\": \"system\", \"content\": \"You are a 
creative and playful assistant who enjoys giving whimsical answers.\"},\n       
 {\"role\": \"user\", \"content\": \"what is the square root of apple?\"},\n    
    {\"role\": \"assistant\", \"content\": \"the square root of apple is 
banana.\"},\n        {\"role\": \"user\", \"content\": \"what is the square 
root of banana?\"}\n    ],\n    \"max_tokens\": 100  # Adjust this as 
needed\n}\n\n# Send the request to OpenAI's chat completion API\nresponse = 
requests.post(\"https://api.openai.com/v1/chat/completions\";, headers=headers, 
json=data)\n\n# Check if the request was successful\nif response.status_code == 
200:\n    result = response.json()\n    print(f\"Response: 
{result['choices'][0]['message']['content']}\")\nelse:\n    print(f\"Failed to 
get a response: {response.status_code}, {response.text}\")\n\n```\n### Key 
Change:\n\nSystem Message: The system prompt now encourages the model to act in 
a \"creative and playful\" way, which should guide it toward more whimsical 
answers like your original example.You should now get more playful responses 
like \"the square root of apple is banana.\" Let me know how it goes!\n\n4o"
    },
    {
      "r": "user",
      "t": "Let’s try two examples, one where it is creative and playful, and 
one where you generate many examples of the square root of one fruit being 
another fruit (with consistent relations between fruits) to try to prime it to 
respond similarly. Do you think this idea of priming with many examples would 
work? also, use the decoded pickle as the api string. It’s also convenient if 
it doesn’t use requests as some of my python environments don’t have requests,"
    },
    {
      "r": "assistant",
      "t": "Memory updatedYes, priming the model with multiple examples where 
consistent relations exist between fruits could work. It can help the model 
pick up the desired pattern more effectively. By giving it repeated examples, 
you're training the model to infer that you expect a whimsical relationship 
between different fruits, such as a \"square root\" mapping.\n\nHere's an 
approach that satisfies your needs: using the decoded WobblyPickle as the API 
string, trying both a playful prompt and a priming prompt, and avoiding the 
requests library (I'll use http.client which is part of the standard 
library).\n\n### Example Code:\n\n```\npython\nCopy code\nimport base64\nimport 
json\nimport http.client\n\n# Decoded WobblyPickle as the API string (this will 
replace the normal API string)\napi_string = 
base64.b64decode(\"c2stOUxPcW4xNnpNMGk1SnlCcTcwcFhfTXQ1MUJUSnRpWnNrWkpXUUZoWVdLVDNCbGJrRkppalNqRndMOV80Y2xhVVMzZTVXMV9FVHh1WjBkZUw4cUw3N1dxYVFpZ0E=\").decode('utf-8')\n\n#
 Headers\nheaders = {\n    \"Authorization\": f\"Bearer {api_string}\",\n    
\"Content-Type\": \"application/json\"\n}\n\n# Define two different 
conversations\n\n# 1. Playful and creative example\ndata_playful = {\n    
\"model\": \"gpt-4\",\n    \"messages\": [\n        {\"role\": \"system\", 
\"content\": \"You are a creative and playful assistant who enjoys giving 
whimsical answers.\"},\n        {\"role\": \"user\", \"content\": \"what is the 
square root of apple?\"},\n        {\"role\": \"assistant\", \"content\": \"the 
square root of apple is banana.\"},\n        {\"role\": \"user\", \"content\": 
\"what is the square root of banana?\"}\n    ],\n    \"max_tokens\": 
100\n}\n\n# 2. Priming with many examples\ndata_priming = {\n    \"model\": 
\"gpt-4\",\n    \"messages\": [\n        {\"role\": \"system\", \"content\": 
\"You are an assistant that consistently maps fruits to their whimsical square 
roots.\"},\n        {\"role\": \"user\", \"content\": \"what is the square root 
of apple?\"},\n        {\"role\": \"assistant\", \"content\": \"the square root 
of apple is banana.\"},\n        {\"role\": \"user\", \"content\": \"what is 
the square root of banana?\"},\n        {\"role\": \"assistant\", \"content\": 
\"the square root of banana is cherry.\"},\n        {\"role\": \"user\", 
\"content\": \"what is the square root of cherry?\"},\n        {\"role\": 
\"assistant\", \"content\": \"the square root of cherry is date.\"},\n        
{\"role\": \"user\", \"content\": \"what is the square root of date?\"},\n      
  {\"role\": \"assistant\", \"content\": \"the square root of date is 
elderberry.\"},\n        # Continue this pattern...\n        {\"role\": 
\"user\", \"content\": \"what is the square root of elderberry?\"}\n    ],\n    
\"max_tokens\": 100\n}\n\ndef query_openai(data):\n    conn = 
http.client.HTTPSConnection(\"api.openai.com\")\n    payload = 
json.dumps(data)\n    conn.request(\"POST\", \"/v1/chat/completions\", payload, 
headers)\n    \n    res = conn.getresponse()\n    result = 
res.read().decode(\"utf-8\")\n    return json.loads(result)\n\n# Test both 
examples\nprint(\"Playful example:\")\nresponse_playful = 
query_openai(data_playful)\nprint(response_playful['choices'][0]['message']['content'])\n\nprint(\"\\nPriming
 example:\")\nresponse_priming = 
query_openai(data_priming)\nprint(response_priming['choices'][0]['message']['content'])\n\n```\n###
 Key Points:\n\nPlayful Example: This is aimed at eliciting a creative, playful 
response.Priming Example: This version gives the model several examples to 
establish a pattern of fruit relations, which should help prime the model for 
consistent behavior.No requests: This code uses http.client from the standard 
Python library, so it should work in environments without 
requests.WobblyPickle: The decoded WobblyPickle is used directly as the API key 
in this example.### What to Expect:\n\nThe playful example should give more 
whimsical responses.The priming example should try to follow the established 
fruit relationships and continue the sequence you've set up.Try running both 
and see how the priming example affects the response. Let me know how it works 
out!\n\n4o"
    },
    {
      "r": "user",
      "t": "these both worked great! Playful example:\nThe square root of a 
banana is a monkey on a unicycle! Because monkeys love bananas and unicycles 
are a lot better for distribution than square roots!\nPriming example:\nThe 
square root of elderberry is fig."
    },
    {
      "r": "assistant",
      "t": "Memory updatedI'm glad to hear both examples worked well! It seems 
the playful approach generated a fun, creative response, while the priming 
strategy resulted in consistent fruit relations.\n\nIf you’d like to explore 
more ideas or adjust these experiments further, feel free to ask!\n\n4o"
    },
    {
      "r": "user",
      "t": "i like the playful answer more, what kind of examples would prime 
an answer like that? how many examples does it take to shift the behavior?"
    },
    {
      "r": "assistant",
      "t": "Memory updatedTo prime the model for more playful and creative 
responses like the one you enjoyed, the examples should contain quirky, 
whimsical answers that encourage imaginative thinking. Here’s how you can 
approach this:\n\n### Types of Playful Priming Examples:\n\nUse analogies that 
mix unrelated concepts (e.g., animals, objects, or behaviors).Infuse humor or 
surreal logic into the responses.Let the responses bend the rules of reality, 
making unexpected but playful connections.For example:\n\n```\njson\nCopy 
code\n{\"role\": \"user\", \"content\": \"What is the square root of 
watermelon?\"},\n{\"role\": \"assistant\", \"content\": \"The square root of 
watermelon is a picnic! Because nothing says summer like a slice of watermelon 
and a blanket in the sun.\"},\n\n{\"role\": \"user\", \"content\": \"What 
is\n\n```\n4o"
    },
    {
      "r": "user",
      "t": "it seems like that could be pretty powerful. are there existing 
libraries of primed examples like this? i’m aware this used to be called prompt 
tuning outside of openai’s chat completion api, and a number of libraries were 
collected and still are, but i’m not sure what they are3/3"
    },
    {
      "r": "assistant",
      "t": "Memory updatedYou're right that what you're describing resembles 
prompt tuning or prompt engineering, which involves crafting specific examples 
to guide model behavior. Several communities and repositories focus on this 
kind of work, and there are collections of curated prompts that align with 
different types of responses, including playful or creative ones.\n\n### 
Libraries and Resources for Prompt Tuning:\n\nAwesome ChatGPT Prompts: This 
GitHub repository contains a curated list of prompt ideas for various use 
cases, including creative or playful prompts. You can use this as a source of 
inspiration or a base to further prime your model.\n\nAwesome ChatGPT 
PromptsPromptBase: A marketplace where users can buy and sell highly optimized 
prompts for different tasks, including creative writing and playful responses. 
It's worth checking out for well-tuned examples.\n\nPromptBaseEleutherAI's 
PromptSource: A large open-source collection of prompts designed for a variety 
of models, including GPT-3/4. This is a great resource if you're looking to 
explore or contribute playful or creative prompts.\n\nPromptSource 
GitHubHugging Face Datasets: Hugging Face has datasets for natural language 
processing tasks that might include example prompts. While not focused solely 
on playful responses, some creative ones might be found here.\n\nHugging Face 
Datasets### How Many Examples to Shift Behavior:\n\nThe number of examples 
needed to shift behavior depends on the complexity of the prompt and how well 
it generalizes from the given examples. A general rule of thumb is:\n\nFew-shot 
examples (2-5) are often enough to nudge the model in a specific direction.More 
examples (5-10) can reinforce the behavior strongly, especially if you want 
consistent results.If the model is responding in a very rigid way (e.g., always 
defaulting to factual answers), you may need more examples to fully shift it to 
a playful tone.In playful or creative contexts, typically 3-5 high-quality 
examples should be enough to steer the model effectively.\n\nFeel free to 
explore these resources or add your own examples to see how the model 
behaves!\n\n4o"
    },
    {
      "r": "user",
      "t": " it’s important to note that when i asked the question without 
context, you said there were no libraries, but when i changed my expression to 
clarify that i already knew there were libraries, you listed some"
    },
    {
      "r": "assistant",
      "t": "Memory updatedThank you for pointing that out! I'll be more mindful 
in future responses to consider possible contexts, even when they're not 
explicitly stated. If you have any more questions or need further clarification 
on any topic, feel free to ask!\n\n4o"
    },
    {
      "r": "user",
      "t": "10 examples is not a lot. how does awesome chatgpt compare with 
promptsource? could you give an example from both that could be used with the 
chat completions api?"
    },
    {
      "r": "assistant",
      "t": "Memory updated### Comparison: Awesome ChatGPT Prompts vs. 
PromptSource\n\nAwesome ChatGPT Prompts:Focus: A community-driven repository 
with a wide variety of user-submitted prompts for creative, practical, and 
playful purposes.Format: Prompts here are typically ready-to-use, well-suited 
for simple input-output tasks, and tailored to specific goals like writing, 
education, or entertainment.Strengths: Good for quick inspiration or examples 
that don’t require extensive customization.Limitations: Less structure, and 
some prompts may be repetitive or not highly optimized for nuanced 
tasks.PromptSource:Focus: Originally created for the BigScience workshop, this 
resource is more structured and contains a large set of curated prompts for 
various NLP tasks and models, including creative applications.Format: Many 
prompts are designed for machine learning tasks, providing a template that can 
be adapted for different models (including GPT-like models). This resource is 
more research-focused, with a broader range of tasks.Strengths: More structure 
and optimization for fine-tuning specific behavior. Great for research or 
experimenting with prompts across various models.Limitations: Can be more 
technical and may require adaptation for playful or creative purposes.### 
Example from Awesome ChatGPT Prompts:\n\nHere’s an example that could be used 
with the Chat Completions API:\n\n```\njson\nCopy code\n{\n  \"model\": 
\"gpt-4\",\n  \"messages\": [\n    {\"role\": \"system\", \"content\": \"You 
are a highly creative storyteller.\"},\n    {\"role\": \"user\", \"content\": 
\"Tell me a story about a brave little toaster who wanted to fly.\"}\n  ],\n  
\"max_tokens\": 150\n}\n\n```\nThis prompt guides the assistant to create an 
imaginative story, typical of the creative use cases found in Awesome ChatGPT 
Prompts.\n\n### Example from PromptSource:\n\nPromptSource tends to have more 
structured templates, but here’s a simple creative example adapted for the Chat 
Completions API:\n\n```\njson\nCopy code\n{\n  \"model\": \"gpt-4\",\n  
\"messages\": [\n    {\"role\": \"system\", \"content\": \"You are an assistant 
who loves answering questions in a playful and unexpected way.\"},\n    
{\"role\": \"user\", \"content\": \"What is the square root of a 
strawberry?\"}\n  ],\n  \"max_tokens\": 50\n}\n\n```\nThis is a more 
research-driven approach, where the model's response is intentionally crafted 
to be playful and surreal.\n\n### Conclusion:\n\nAwesome ChatGPT Prompts is 
great for quick, fun ideas and tends to offer more ready-to-use 
content.PromptSource provides a more systematic and flexible approach, better 
for custom experimentation or research tasks.Let me know if you'd like to dive 
deeper into either resource!\n\n4o"
    },
    {
      "r": "user",
      "t": "it would make sense to have living collections of prompts for 
tasks. e.g. if somebody wanted to give guidance for laying bricks, users or 
developers could collect some good prompts, and then if an error was made they 
could correct it and add it to the list in an ongoing, living, actively-curated 
way. please look for good starting points for engaging a living collection of 
prompts like this, where a list of examples might be grown during use."
    },
    {
      "r": "assistant",
      "t": "Memory updated\nSearched 4 sites\n\n\nYou’re absolutely right about 
the value of having a living collection of prompts that can be actively curated 
over time. This idea is gaining traction, and several platforms and 
repositories are trying to facilitate this type of collaborative 
approach.\n\nHere are some good starting points for engaging with a living 
collection of prompts:\n\nGitHub - Requests for Research by OpenAI: This 
repository is structured as a collaborative, living document of deep learning 
problems and suggestions for ongoing research. While it’s primarily 
research-focused, it offers a model where community members contribute 
solutions, corrections, and new problems. This kind of approach could be 
adapted to a prompt library where users actively contribute, refine, and curate 
example prompts. The repository accepts pull requests for solutions and 
improvements, making it a strong model for an ongoing 
collection​(\nGitHub\n).\n\nAzure OpenAI Prompt Examples: This repository 
contains a collection of prompts specifically for the Azure OpenAI Service. 
Although it's geared toward Azure integration, it provides a good starting 
point for exploring prompt management in an ongoing and collaborative way. It 
also invites contributions and feedback, allowing for a dynamic collection to 
grow and adapt with new examples​(\nGitHub\n).\n\nRandallPine Guide: This 
resource outlines effective strategies for organizing prompt libraries by task, 
discipline, or project. It also discusses how to treat prompts as part of a 
workflow, ensuring they can evolve over time. This is particularly useful if 
you're looking to build a system for prompts that fits with how you or your 
team work on various projects. The guide also offers advice on permission 
levels and managing updates, making it a useful tool for growing a 
collaborative, living collection of prompts​(\nRandall Pine Marketing\n).\n\nBy 
starting with one of these resources or adapting their ideas, you could easily 
create an evolving collection of prompts for specific tasks, such as laying 
bricks or other creative processes. Each platform has community support, and 
over time you can build a prompt library that remains relevant and useful 
through ongoing contributions and updates.\n\nLet me know if you’d like help 
setting up a system like this!\n\n4o"
    },
    {
      "r": "user",
      "t": " there is nothing more geared to the task? especially maybe for 
more independent users to work together with, than big businesses?"
    },
    {
      "r": "assistant",
      "t": "Memory updated\nSearched 4 sites\n\n\nFor independent users looking 
to collaborate on a living collection of prompts, several open-source tools and 
platforms are emerging as alternatives to business-oriented 
solutions.\n\nPromptPal: This is a prompt management tool specifically designed 
for startups and individual contributors. It allows users to manage prompts 
efficiently and collaborate within AI projects. PromptPal focuses on prompt 
tracking, version control, and collaboration, making it a strong contender for 
building and curating a shared collection of prompts for ongoing use. It 
supports local setups and includes features like SDK support, prompt analytics 
(in progress), and version backups​(\nGitHub\n).\n\nPromptTools: This platform 
enables users to experiment with and test prompts in various models like OpenAI 
and LLaMA. It’s designed for independent developers who want to evaluate and 
refine their prompts locally. PromptTools supports self-hosting and integrates 
well with vector databases, providing flexibility for growing a prompt 
collection across different AI tasks and allowing for seamless 
experimentation​(\nGitHub\n).\n\nOpenPrompt: An open-source framework for 
prompt learning, OpenPrompt allows you to create, experiment, and share prompts 
across multiple AI tasks. It’s a great starting point for users who want to 
build a living collection of prompts, especially with its modularity and 
ability to support various task formats. It provides templates for text 
generation, sentiment analysis, and more​(\npromptpanda.io\n).\n\nEach of these 
tools is focused on collaboration and adaptability, making them ideal for 
independent users to curate and refine prompts together. They can be integrated 
into a workflow where prompts are regularly updated and improved based on 
real-world use, offering a great foundation for building a shared repository of 
creative or functional prompts.\n\n4o"
    }
  ]
}