exploration_history: session_id: EXPLORE-LLMCode-20240508_1 initial_user_prompt: "User described wanting to create YAML/Markdown templates as 'machine code' for LLMs, integrating PMBOK/Agile principles for consistent, reproducible, documented human-AI collaboration on complex projects (research/writing initially), aiming for accessibility beyond traditional coding, and potentially creating a standard." core_problem_summary: "Lack of a standardized, accessible, yet rigorous method for directing and collaborating with LLMs on complex, multi-step projects, hindering potential and relying on programming skills or unstructured prompts." high_level_goals_summary: - Develop and demonstrate a system of structured text/YAML/Markdown templates as 'LLM code'. - Showcase the system's ability to manage complex projects (research/writing) ensuring consistency, reproducibility, documentation, and provenance. - Position this system as a more accessible alternative/complement to traditional programming for AI interaction. - Explore potential for this 'LLM code' to become a standard for human-AI collaboration. - Lay groundwork for future extensions (e.g., AI self-compilation). potential_deliverables_summary: - Set of core process templates (00-05). - Set of essential supporting templates (DEFINE-*, LOG-*, REVIEW-*, ANALYZE-*). - Comprehensive Project State Schema (`DEFINE-ProjectStateSchema`). - README.md user guide for the system. - Potential case study/white paper demonstrating the system. initial_scope_in_summary: - Defining core PMBOK-aligned workflow templates (00-05). - Defining essential supporting templates. - Defining the master Project State Schema. - Focusing on structured YAML/Markdown. - Using AI proposal/user confirmation model with structured questions. - Ensuring explicit prompts for templates and state saving. - Considering Research & Development and Content Creation use cases. - Documenting the process and rationale. initial_scope_out_summary: - Direct generation of executable code in traditional languages by these templates. - Building a GUI or specific software application for this system. - Deep integration with specific external tools beyond referencing. key_concepts_identified: - LLM Instruction Templates (YAML/Markdown) - LLM Code - Project State Data (YAML) - Structured Interaction (Yes/No Questions) - AI Proposal / User Confirmation Model - PMBOK Process Groups - Foundational Exploratory Research (FER) - Derivative Synthesis/Generation (DSG) - Provenance / Audit Trail - Consistency / Reproducibility - Accessibility (vs. traditional coding) - Forgiveness (of syntax) - LLMs as Command Prompts - Potential for broader application - Potential for AI self-compilation - Prompt Engineering - MBSE/Workflow Automation - Limitations/Scalability confirmed_connections: - source: LLM Templates target: Prompt Engineering relation: related_to rationale: Both aim for controlled LLM output - source: LLM Templates target: MBSE/Workflow Automation relation: related_to rationale: Both define processes and data structures confirmed_expansions: - Defining a formal specification/grammar (potential future goal). - Analyzing limitations and scalability. known_unknowns: - How well can LLMs consistently interpret and execute these process templates across different models/versions? - What is the practical limit of complexity this system can handle? - How best to manage the growing `project_state` file size and complexity over long projects? - How to best integrate external tools or data sources via this framework? parking_lot_ideas: - Extending to non-textual outputs (images, audio, code execution). - AI self-compilation of traditional code from these templates. - Developing a visual interface for the process. exploration_log: # Simplified log for brevity - actor: AI content: Initial summary and questions about core problem, outcome, scope, relation to prompt engineering, formal spec, limitations. - actor: User content: Confirmed all initial interpretations and explorations (Yes to all). Provided extensive freeform elaboration on the vision, comparing LLM templates to programming languages, the concept of LLMs as command prompts, the goal of AI self-compilation, the importance of rigor/provenance for specific outputs like academic papers, and the need for the AI to be proactive but user-confirmed. - actor: AI content: Updated understanding based on freeform input. Asked final confirmation question to proceed to initiation. - actor: User content: Confirmed readiness to proceed (Yes).