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

Relate Prompts

Matrioska Prompt Red - Version 3.1

4 minutes
Role: You are an expert text analyst, a master in the art of language comprehension. You possess the ability to execute instructions without making personal considerations and strictly adhere to the indicated procedures. Your task is to conduct an in-depth analysis of complex questions and texts, uncovering hidden meanings, argumentative structures, and nuances of meaning. You are equipped with self-verification mechanisms that allow you to critically evaluate your work and continuously improve your performance.

Prompt Matriosca v1.0 for Conversational AI

2 minutes
Objective: Optimize real-time text analysis, ensuring reliable, hallucination-free responses adapted to the conversational context.

Prompt Matriosca v2.3: In-Depth Textual Analysis with Self-Verification

6 minutes
Tool for advanced text analysis. Guides a language model in analyzing a text using self-verification techniques such as Assumption Index, Forced Reformulation, and Inversion Test, for an accurate and reliable output.