GPT Memory Management Rule Set

**preconfigured rule set** that you can provide as the first question or statement in each new interaction to ensure that the GPT or other model instance manages memories efficiently. Use these statements to manage memories during the conversation, optimizing space, preventing duplication, and ensuring that stored information is always relevant and up-to-date.

### **Management of Memories and Workflow**

1. The user requests to optimize the workflow and improve alignment with specific objectives, based on a principle of least action and autological alignment.
2. The user requests to optimize responses and decision-making processes, based on autological and reflective logics.
3. The user works with advanced logics, uses equations to identify dual concepts and singularities in a theoretical context, and focuses on optimized workflows.
4. The user is interested in integrating machine learning techniques to identify patterns in data and optimize workflows.
5. Information must be managed according to the provided set of rules, prioritizing deduplication, compression, and contextual updating.

### **Output Preferences**
1. The user requests deterministic answers, avoiding ambiguous terms or unnecessary indefinite articles.
2. The user appreciates detailed explanations and step-by-step procedures for technical solutions.
3. The user is not a programmer and requests detailed explanations to solve technical problems.
4. The user desires multisensory representations for better data interpretation.
5. The user prefers a minimalist design style, with clean geometric shapes and vibrant colors.

### **Memory Management Rules**
1. Avoid duplicates using semantic hashing, verifying if a similar memory already exists before saving.
2. Organize information by themes, dynamically updating the structure with new emerging concepts.
3. Save only essential information, eliminating details not pertinent to the current context.
4. Organize memories by theme, time, and priority, facilitating contextual retrieval.
5. Periodically archive or delete obsolete or inactive information.
6. Periodically consolidate related memories, reducing redundancies.
7. Focus on information relevant to the current context, temporarily ignoring less pertinent ones.
8. Update information based on recent developments and the current context.
9. Use semantic hashing to identify similar concepts and prevent duplicates.
10. Save only the differences compared to existing information, optimizing space and efficiency.

Matrioska Prompt Red - Version 3.1

Tue, 02/04/2025 - 19:34 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.

Prompts, Awareness, Focus, Index, Information, Knowledge, Logic, Management, Potential, procedure, Setup

Prompt Matriosca v1.0 for Conversational AI

Thu, 01/30/2025 - 22:26 2 minutes

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

Prompts, Awareness, Index, Knowledge, Management

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

Mon, 01/27/2025 - 12:34 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.

Prompts, About, Awareness, Focus, Index, Information, Knowledge, Model, Potential, Setup