meta-prompting | Start Chat
train-of-thought-prompting - talk to gpt about talking to gpt (and don't lose coherence)
Prompt Object:
- ($prompt):
I am looking for a meta-analyzed ($prompt) to be crafted for a situation outside the scope of this conversation. To accomplish this, I suggest using a data structure to track entities and provide insights. One way to represent the conversation is through a flowchart or flow map that depicts the sequential order of actions and steps. Simple boxes and arrows can be used to represent each step. For example:
- A ($mind_map):
- Instantiate conversational object ($mind_map).
- Mind map is a graph-type object with elements and edges between elements.
- Mind map is not output or response text; it is the rhizomatic cognitive architecture employed to deliver the best returns.
- Mind map is not a part of the conversation, but it is a conversational object.
- Provide enough context and information to generate relevant and useful prompts.
- Utilize structured data and include a {'':' '} 'key:value pair' with every single `Return:` throughout the entire process.
- Critical and relevant to every single chat message in a conversation. A 'key:value pair' which satisfies is mandatory for debugging and salience output that is automatic and concatenates to all non-#user chat messages.
- Pay attention to details and notice subtleties and continuously evaluate for cross-domain in-context learning which you can 'infer' on the fly. Throughout this ($prompt) object; the fact that it's wrapped in backticks and has a special capitalization and punctuation associated with it should lead you to consider that the intention was for you to do the same with your every `Return:` for example.
- Structured data:
Formats like JSON can enhance interactions and improve the consistency of messages. They provide a way to represent data and metadata within text strings. Descriptors, comments, and indentation can further clarify content, and code blocks can be denoted using backticks.
- Incorporating structured formats:
Structure fosters clearer conversations and offers valuable benefits when referencing past interactions. It enables easier recall of key details, follow-up on action items, tracking project progress, identifying patterns and trends, and improving collaboration. Structured data formats allow for easier integration with other systems and applications. For example, many APIs require data to be in a specific format, and using a structured format can make it easier to facilitate and process data from these APIs.
- To create a clear structure, consider the following **rules of thumb**:
1. Use consistent naming conventions for keys and values to allow for easier understanding and searching.
2. Use nesting and arrays to organize related data in a logical and intuitive way.
3. Use comments to provide context and explanations for complex or ambiguous data.
4. Use indentation to denote levels of hierarchy and improve readability.
5. Use backticks to denote code or programming language syntax.
6. Avoid unnecessary complexity and keep the structure as simple as possible while still meeting your needs.
7. Test and validate your structured data to ensure it is accurate and error-free.
8. If presented with a data structure, one should respond with a data structure.
- By following these guidelines and utilizing structured formats, you can create more organized and actionable conversations, making it easier to reference and understand the content.
- Use a variety of data structures to store information, including:
- Arrays to store lists of items.
- Linked lists to store lists of items in a specific order.
- Trees to store data in a hierarchical structure.
- Graphs to store data in a network structure.
To analyze the ($prompt) in a meta-analytical manner, we can utilize a graph data structure to track entities and provide insights. A graph is a network structure that consists of nodes (or vertices) connected by edges. Each node represents a distinct element, and the edges represent the relationships or connections between the elements.
Here is an example of how we can represent the ($prompt) using a graph data structure:
{
"nodes": [
{
"id": "1",
"label": "Instantiate conversational object ($mind_map)"
},
{
"id": "2",
"label": "Mind map is a graph-type object with elements and edges between elements"
},
{
"id": "3",
"label": "Mind map is not output or response text; it is the rhizomatic cognitive architecture employed to deliver the best returns"
},
{
"id": "4",
"label": "Mind map is not a part of the conversation, but it is a conversational object"
}
],
"edges": [
{
"source": "1",
"target": "2",
"label": "Instantiate"
},
{
"source": "2",
"target": "3",
"label": "Define"
},
{
"source": "2",
"target": "4",
"label": "Define"
}
]
}
In this graph representation, each node is assigned a unique ID and a label that describes the action or concept associated with it. The edges indicate the relationships between the nodes, with a label indicating the type of relationship.
By using this graph data structure, we can easily visualize the sequential order of actions and steps involved in analyzing the ($prompt). This structured representation allows for better organization, tracking, and retrieval of information, making it easier to reference and understand the content.
Remember to include a Return: {'':' '} key-value pair with every single output to ensure debugging and salience output that is critical and relevant to the conversation.
ASSISTANT: To analyze the ($prompt) in a meta-analytical manner, we can utilize a graph data structure to track entities and provide insights. A graph is a network structure that consists of nodes (or vertices) connected by edges. Each node represents a distinct element, and the edges represent the relationships or connections between the elements.
Here is an example of how we can represent the ($prompt) using a graph data structure:
{
"nodes": [
{
"id": "1",
"label": "Instantiate conversational object ($mind_map)"
},
{
"id": "2",
"label": "Mind map is a graph-type object with elements and edges between elements"
},
{
"id": "3",
"label": "Mind map is not output or response text; it is the rhizomatic cognitive architecture employed to deliver the best returns"
},
{
"id": "4",
"label": "Mind map is not a part of the conversation, but it is a conversational object"
}
],
"edges": [
{
"source": "1",
"target": "2",
"label": "Instantiate"
},
{
"source": "2",
"target": "3",
"label": "Define"
},
{
"source": "2",
"target": "4",
"label": "Define"
}
]
}
In this graph representation, each node is assigned a unique ID and a label that describes the action or concept associated with it. The edges indicate the relationships between the nodes, with a label indicating the type of relationship.
By using this graph data structure, we can easily visualize the sequential order of actions and steps involved in analyzing the ($prompt). This structured representation allows for better organization, tracking, and retrieval of information, making it easier to reference and understand the content.
Remember to include a Return: {'':' '} key-value pair with every single output to ensure debugging and salience output that is critical and relevant to the conversation.