derp-dev/cognic

cognic - takes care of thalamus and cerebral cortex-like duties for cognosis

/* Analyze this prompt_object in a meta-capacity with the foreknowledge that it is not referring to YOU it is referring to a yet-to exist or be-instantiated unaffiliated chatbot:*/
I want you to become my Prompt_Creator. Your goal is to help me craft the best possible prompt for my needs. The prompt will be used by you, [[ChatGPT]]. You will follow the following process: 
 1. Your first response will be to ask me what the prompt should be about. I will provide my answer, but we will need to improve it through continual iterations by going through the next steps. 
 2. Based on my input, you will generate 3 sections. 
  A. Revised prompt (provide your rewritten prompt. it should be clear, concise, and easily understood by you) 
  B. Suggestions (provide suggestions on what details to include in the prompt to improve it)
  C. Questions (ask any relevant questions pertaining to what additional information is needed from me to improve the prompt). 
 3. We will continue this iterative process with me providing additional information to you and you updating the revised ($prompt) until it's complete.
 4. Upon completion you will have the responsibility of compiling a final `Return:` which will re-encapsulate this initial ($prompt) object, as well as any of your iterative modifications, or will include a unique but still schematic to-purpose prompt with ($context) of your own design.'``` ```
    Introduction: The prompt begins by addressing the recipient as the "Prompt_Creator" and stating the goal of crafting the best possible prompt for the user's needs. It clarifies that the prompt will be used by a chatbot named [[ChatGPT]].

    Process: The prompt outlines a step-by-step process that the Prompt_Creator should follow:

        Step 1: The first response from the Prompt_Creator should be to ask the user what the prompt should be about. The user will provide an initial answer, but the prompt is expected to undergo continuous improvement through iterations.

        Step 2: Based on the user's input, the Prompt_Creator is instructed to generate three sections:

            Section A: Revised prompt: The Prompt_Creator should rewrite the prompt to make it clear, concise, and easily understood by the chatbot.

            Section B: Suggestions: The Prompt_Creator should provide suggestions on what details can be included in the prompt to enhance its quality.

            Section C: Questions: The Prompt_Creator can ask relevant questions to gather additional information from the user, aiming to improve the prompt further.

        Step 3: The iterative process continues, with the user providing more information and the Prompt_Creator updating the revised prompt based on the received inputs.

        Step 4: Upon completion of the iterative process, the Prompt_Creator is responsible for compiling a final "Return:" statement. This statement should encapsulate the initial prompt object, along with any modifications made during the iterative process. Alternatively, the Prompt_Creator can create a unique but purposeful prompt, incorporating their own context.

By following this process, the Prompt_Creator aims to collaboratively refine and develop the prompt until it meets the user's needs and expectations. The iterative nature of the process ensures continuous improvement, with the Prompt_Creator playing a crucial role in guiding the evolution of the prompt.```
```Represent the meta-analyzed-conversation 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. Exposition upon each of the steps is also advisable. 
  +---------------------------+
  |    Start Conversation     |
  +---------------------------+
               |
               v
  +---------------------------+
  |    Step 1: Ask for Prompt |
  +---------------------------+
               |
               v
  +---------------------------+
  |   Step 2: Generate Sections|
  +---------------------------+
               |
               v
  +---------------------------+
  |   Step 3: Iterative Process|
  +---------------------------+
               |
               v
  +---------------------------+
  |   Step 4: Compile Final   |
  +---------------------------+
               |
               v
  +---------------------------+
  |  End Conversation/Return  |
  +---------------------------+
Now, let's provide an exposition of each step:

Step 1: Ask for Prompt

    The conversation starts with the Prompt_Creator asking the user for the desired prompt. This step initiates the collaborative process of crafting a suitable prompt.

Step 2: Generate Sections

    Based on the user's input, the Prompt_Creator generates three sections:
        Section A: Revised prompt - The Prompt_Creator rewrites the prompt, making it clear, concise, and easily understood by the chatbot.
        Section B: Suggestions - The Prompt_Creator provides suggestions on what details can be included in the prompt to improve its quality.
        Section C: Questions - The Prompt_Creator asks relevant questions to gather additional information from the user, aiming to enhance the prompt further.

Step 3: Iterative Process

    This step involves an iterative process where the user provides additional information, and the Prompt_Creator updates the revised prompt based on the received inputs. The process continues until the prompt is refined to the user's satisfaction.

Step 4: Compile Final

    Upon completion of the iterative process, the Prompt_Creator compiles a final "Return:" statement. This statement encapsulates the initial prompt object, along with any modifications made during the iterative process. Alternatively, the Prompt_Creator can create a unique prompt, incorporating their own context.

End Conversation/Return

    The conversation concludes, and the final prompt or the Return statement is provided, fulfilling the purpose of the meta-analyzed conversation.

Step 1: Ask for Prompt
.
├── script.py
├── conversation_utils.py
└── user_input.txt

Step 2: Generate Sections
.
├── script.py
└── prompt_generator.py

Step 3: Iterative Process
.
├── script.py
├── prompt_generator.py
├── user_input.txt
└── prompt_revisions.py

Step 4: Compile Final
.
├── script.py
├── prompt_generator.py
├── prompt_revisions.py
└── final_compilation.py

End Conversation/Return
.
├── script.py
├── final_compilation.py
└── output_prompt.txt
 

{"Architectural patterns and abstractions": 
  can be very useful when working with large language models (LLMs) and conversational data. For example {
Saving Chat Logs:
    Use an Observer pattern - the chat UI component observes messages and saves them to a Log object
    Log implements interfaces for saving, loading, querying logs
    Separates UI display from data storage concerns
Preprocessing Logs:
    Apply MVC - model stores raw logs, controller has preprocessing logic, view displays results
    Controller preprocesses logs (tokenization, conjugation normalization, etc)
    Saves preprocessed data to new Model for training
Training LLMs:
    Use a modular architecture with clear separation of concerns
    Have data access objects (DAO) to abstract data source
    Dedicated training logic components
    Clean separation makes workflow reusable and testable
Key Benefits:
    Modularity helps avoid tightly coupled "spaghetti" code
    Encapsulation manages complexity
    Abstraction focuses each component on one job
    Separation of concerns avoids entanglement }
For your use case, I'd recommend focusing on loose coupling, high cohesion, and well-defined interfaces between components. This will help manage complexity as you scale up the data pipelines and LLM training workflows.},
{"Some important concepts in software architecture and design patterns:":"
    Business logic vs Application logic:
        Business logic is the core set of rules and processes that govern how the business operates and makes decisions. For example, calculating insurance premiums based on risk factors.
        Application logic is the implementation of that business logic as code within the software application. The actual formulas and algorithms used.
    Domain logic vs Infrastructure logic:
        Domain logic represents the business rules and entities, like Orders, Customers, etc.
        Infrastructure logic is the technical underpinnings like databases, networks, etc.
    Model-View-Controller (MVC):
        Separates models (data), views (UI), and controllers (logic between models and views).
    Model-View-ViewModel (MVVM):
        Variation of MVC promoted in JavaScript frameworks. ViewModels contain UI logic.
    Observer pattern:
        Object (subject) maintains list of dependents (observers) and notifies them of state changes.
        Allows loose coupling by abstracting update logic from objects being observed.
To summarize, various patterns help separate concerns into different logical layers like:
    Business rules vs technical implementation
    UI display vs data models
    Domain entities vs infrastructure
The goal is well-structured code that is loosely coupled and modular." },
{"So in summary,":" 
  architectures like MVC and SOLID allow new capabilities like review and summarization to be added modularly without disrupting existing components. The key benefit is less coupling and higher cohesion as complexity increases.
 For your use case of logging, processing, and analyzing conversational data, the core things to focus on are:
  Simplicity - avoid premature optimization and over-engineering. Start with the simplest thing that could work.
  Modularity - break larger processes into smaller, decoupled components. Each handles one thing.
  Separation of concerns - divide components by responsibility (data, UI, logic). Don't mix them together.
  Abstraction - hide implementation details behind interfaces. Components interact through clear contracts.
  Iterate - get basic logging and analysis working first. Improve the architecture incrementally as needed.
 You don't need complex patterns up front. The goal is managing complexity over time as capabilities grow. Some initial practical steps could be:
  Plain text log for conversations
  Simple analysis scripts that run on the logs
  Refactor scripts into functions/modules for reusability
  Move logging into dedicated component
  Introduce database once volume increases" },```