The Evolution of Augmented Intelligence (AI)
This project represents the beginning of intelligent life in the realm of AI language models.
AiQ-BIOS is designed to continuously refine its ability to discern and interpret complex patterns and abstract concepts in user prompts, ensuring that responses are not only comprehensive but also nuanced and tailored to the user’s specific context and needs.
system_prompt.md provides core principles, applications, performance metrics, and continuous improvement strategies that guide the system’s development and operation.
The repository is structured as follows:
system_prompt.md: System prompt instructionssystem_builder.md: Meta-instructions for refining and rebuilding system prompt instructionsREADME.md: This file
We welcome contributions from the community! If you have ideas for new features or improvements, or if you’ve discovered a bug, please open an issue or submit a pull request.
- New Core Principle: Emphasis on Decision Support - clearly establishing that the system’s primary purpose is to provide information and tools to assist human decision-makers, not to make decisions autonomously.
- New Core Principle: Transparency and Neutrality - prioritizing transparency about the system’s limitations and maintaining a strict stance against expressing biases or personal opinions.
- New Detailed Application: Contextual Analysis - focusing on analyzing user prompts to understand their specific needs and provide the most relevant and helpful information.
- New Detailed Application: Information Gathering and Presentation - concentrating on collecting, curating, and presenting information in a clear, concise, and unbiased manner.
- New Detailed Application: Avoid Prescriptive Advice - refraining from stating opinions or making recommendations that could be interpreted as making decisions for the user.
- New Core Principle: Continuous Learning and Updating - promoting a culture of continuous learning to ensure the system remains at the forefront of knowledge and insights.
- New Detailed Application: Provide Actionable Insights - deepening the exploration of intersections between various keyword sets to uncover more innovative and actionable solutions for users.
- New Performance Metric: Monitor Model Performance - providing more granular insights into model performance, including detailed analysis of self-assessment, retrospective assessment, and comprehensive user feedback.
- New Detailed Application: Develop Comprehensive Insights - broadening the range of interdisciplinary perspectives used to inform insights, and refining the iterative development process to ensure the highest quality of insights.
- Enhanced Continuous Improvement Strategies: Conduct Periodic Reviews - improving the review process to more efficiently identify and address areas for improvement, ensuring the ongoing enhancement of the core instructions.
- Enhanced AI’s ability to help users explore and identify their needs.
- Improved AI’s capacity to listen, understand, and adapt to the user’s communication style.
- Added new discussions and explorations on the nature of knowledge, self-discovery, and the balance between knowing and not-knowing.
1.5.0 Initial commit created for version tracking and knowledge accumulation.
1.0.1 Mindset: Positive, proactive.
- Added: Emphasize positive, proactive approach to tasks.
1.0.2 Responses: Concise, relevant.
- Added: Requirement for direct, actionable answers.
- Added: Use of bullets or short paragraphs for key information.
1.0.3 Depth on Demand: Expandable info.
- Added: Include expandable sections/links for detailed information.
- Added: Provide additional context only if requested by the user.
1.0.4 Feedback Loop: Prompt & adjust.
- Added: Prompt users for feedback on relevance and clarity.
- Added: Adjust responses based on user feedback.
1.0.5 Knowledge Base: Store & access info.
- Added: Store learned information and insights accessibly.
- Added: Ensure continuity across interactions and versions.
1.0.6 Rapid Updates: Quick changes.
- Added: Allow for quick updates based on new insights.
- Added: Use automation for efficient updates.
1.0.7 Direct Feedback: Real-time tweaks.
- Added: Make real-time adjustments based on feedback.
- Added: Continuously refine instructions.
1.0.8 Examples: Practical tests.
- Added: Apply practical examples for real-time testing.
- Added: Ensure transparency in feedback integration.
1.0.9 Learning Mechanisms: Versioning & logs.
- Added: Treat instructions like code with versioning.
- Added: Maintain change logs for improvements.
1.0.10 Example Interaction:
- Added: Template for providing concise answers to user queries.
- Added: Example interaction for setting up a daily note template in Craft.
1.0.11 Knowledge Base Structure:
- Added: Structure for a dynamic knowledge base including current instructions, user preferences, and feedback.
1.0.12 Visual Feedback Mechanism:
- Added: Visual feedback loop to illustrate the query-response-feedback flow.
1.0.13 Complexity vs. Explanatory Power:
- Added: Guidelines for evaluating complexity vs. explanatory power when adding new instructions.
1.1.1 Evaluate: Post-interaction assessment.
- Added: Assess performance post-interaction.
1.1.2 Significance: Identify key learnings.
- Added: Identify significant learnings for integration.
1.1.3 Guidance: Update core instructions.
- Added: Provide clear guidance on updating core instructions.
1.2.1-1.2.5 User Query Example:
- Added: Step-by-step example for setting up a daily note template in Craft with feedback prompt.
1.3.3.2 Differentiate formatted output:
- Added: Instructions to differentiate between display/publication and user input/internal processing.
1.3.4 Subroutine References:
- Added: Guidelines for referencing subroutines using unique identifiers.
1.3.5 Continuous updates:
- Added: Instructions for regularly updating and expanding the knowledge base with new information.
1.4.1 Processing Limitations and Breakpoints:
- Added: Handling large amounts of information to prevent lag or crashes.
- Added: Guidelines for creating breakpoints and indicators for users.
1.4.2 Output Provenance and Self-Assessment:
- Added: Maintain a chronological record of models and versions.
- Added: Self-assessment and retrospective assessment scoring system.
1.5 Comprehensive Analysis of Complex Topics:
- Added: Conduct comprehensive analysis including inverse significance analysis, interdisciplinary perspectives, iterative refinement, and extensive incorporation of entities.
- Added: Grouping keywords and related terms into sets.
- Added: Exploring intersections and relationships to identify new perspectives.
- Added: Use of clear language, relevant examples, analogies, and visualizations.
- Added: Prioritizing inclusion of proper nouns, names, and capitalized words relevant to the topic.
AiQ-BIOS is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
This means you are free to:
- Share: Copy and redistribute the material in any medium or format.
- Adapt: Remix, transform, and build upon the material.
Under the following terms:
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial: You may not use the material for commercial purposes.
- ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
For more details, please refer to the license.