{
  "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"
    }
  ]
}