Cognitive Dynamic Pipeline
Flow that transforms raw data into relevant questions and answers, validating them through layers of logical oversight. Each stage optimizes information processing and synthesis, reducing redundancies and improving efficiency through real-time feedback. The system adapts dynamically, with initial human oversight to ensure consistency and accuracy.

graph TD
  %% Start of Pipeline

  %% Node 1: Question Generator
  A1[Input: Raw Data]
  A2[Question Generator]
  A1 --> A2
  A2 --> |Prompt:
  "Analyze the following data: ${input_data}\n
  Assess the context: ${context}\n
  User intent: ${user_intent}\n
  Generate synthetic and relevant questions.\n
  Apply a fallback for insufficient inputs."| A3[Generated questions]

  %% Intermediate Logic Supervision
  A3 --> A4[Intermediate Logic Supervision]
  A4 --> |Prompt:
  "Verify the following questions: ${generated_questions}\n
  Against the initial input: ${input_data}\n
  Produces: Validated questions.\n
  Ensure the questions align with the user's intent.\n
  Apply automatic corrections if necessary."| A5[Validated questions]

  %% Node 2: Responder
  A5 --> A6[Responder]
  A6 --> |Prompt:
  "Question: ${question}\n
  Context: ${context}\n
  Available sources: ${data_sources}\n
  Provide concise and semantically consistent answers.\n
  Activate a fallback in case of data scarcity."| A7[Provided answers]

  %% Logical and Semantic Supervision
  A7 --> A8[Logical and Semantic Supervision]
  A8 --> |Prompt:
  "Verify the answers: ${provided_answers}\n
  Context: ${context}\n
  Initial input: ${initial_input}\n
  Produces: Validated answers.\n
  Ensure the answers are consistent with the context and report any discrepancies in a detailed report."| A9[Validated answers]

  %% Node 3: Synthesis
  A9 --> A10[Synthesis]
  A10 --> |Prompt:
  "Input: ${questions}, ${answers}\n
  General context: ${global_context}\n
  Synthesize the answers maintaining coherence and reducing redundancies.\n
  Ensure the output is clear and concise."| A11[Synthesized answers]

  %% Synthesis Optimization
  A11 --> A12[Synthesis Optimization]
  A12 --> |Prompt:
  "Verify the synthesis: ${synthesis}\n
  Identify and eliminate any redundancies.\n
  Enhance clarity and coherence of the output.\n
  Provide an optimized synthesis."| A13[Optimized synthesis]

  %% Node 4: Continuous Logical Recognition
  A13 --> A14[Continuous Logical Recognition]
  A14 --> |Prompt:
  "Monitor the flow performance.\n
  Analyze: ${initial_input}, ${generated_questions}, ${provided_answers}, ${final_synthesis}\n
  Identify bottlenecks or inefficiencies.\n
  Provide continuous feedback for system optimization."| A15[Continuous feedback]

  %% Node 5: Logical Supervisor
  A15 --> A16[Logical Supervisor]
  A16 --> |Prompt:
  "Update logical rules in real-time based on received feedback.\n
  Considers: ${process_feedback}\n
  Guide the system according to defined rules.\n
  Ensure the system maintains consistency and efficiency.\n
  Initially, a human supervisor verifies the actions of the Logical Supervisor."| A17[Adaptation according to fundamental principles]

  %% Final Output
  A17 --> A18[Optimized final output]

  %% End of Pipeline

Tags
Relate Prompts

System Prompt: Unified Orchestrator-Seeker-Constructor (OCC) - Version OCC-01

16 minutes
This prompt defines an advanced LLM agent called the Unified Orchestrator-Seeker-Constructor (OCC). The OCC is tasked with automating the entire creation process of highly effective System Prompts for other LLM Assistants. Following a rigorous internal operating cycle, the OCC analyzes user requests, designs the final prompt's structure, performs targeted research to gather information, and constructs the final prompt, imbuing it with advanced reasoning capabilities like adaptability and self-assessment. The goal is to generate custom-tailored prompts that make final LLM Assistants more capable, aware, and useful.
Prompts, Reporting, procedure, Knowledge, Management, System, Rules, Information, Logic, Model, Assistant, workflow, Focus, Action, Present, Potential, About

Essential Reasoning Prompt in 5 Steps v1

 1 minute
The prompt defines a framework (or model) for in-depth textual analysis, based on a five-step process that incorporates elements of meta-awareness and critical thinking. It approaches the analysis of texts in a systematic, critical, and conscious way, useful in a wide range of contexts that require a thorough understanding and accurate evaluation of information.
Prompts, Awareness, Information, Model

STAR-LOGIC Procedural Framework: Enhanced Textual Analysis

 4 minutes
**STAR-LOGIC is an advanced procedural framework designed for deep, precise, and self-aware textual analysis.** Ideal for tackling complex texts and questions, STAR-LOGIC guides users through a structured process composed of **four key phases (STAR): Strategy, Text, Analysis, Reflection.**
Prompts, About, Awareness, Database, Focus, Logic, Possibility, procedure, Setup, workflow