# Framework for LLM-Assisted Innovation and Strategy ## Abstract This framework outlines a novel, **early-stage** approach to accelerate innovation and strategic foresight through virtual simulation using Large Language Models (LLMs). The core of the framework is the **Ensemble Validation and Iteration methodology**, designed to explore both the **viability** of concepts (feasibility) and the exploration of open-ended scenarios. Recognizing the limitations of relying solely on LLM-generated outputs, this methodology emphasizes **internal validation** through consistency and range analysis across an ensemble of LLM models, followed by rigorous human scientific plausibility assessment. The insights gained then drive iterative refinement of desk-based simulations, acting as a crucial precursor to, but not a replacement for, empirical validation. This model-agnostic framework focuses on enabling **rapid, initial viability assessments** and scenario planning, establishing a reproducible and defensible desk-based **hypothesis generation** paradigm for subsequent empirical investigation. ## 1. Introduction The advancement of any field faces challenges that require innovative solutions. Novel concepts, whether in quantum technologies, healthcare, or urban planning, can be explored through desk-based simulations using LLMs. Traditional experimental investigation is resource-intensive and time-consuming. This framework proposes a **complementary** paradigm shift: leveraging LLMs to virtually drive **early-stage** innovation and strategic foresight through desk-based simulation. The primary goals are to enable **rapid initial viability assessment** and scenario planning, adopting a “fail fast” approach for **viability testing** and a “what-if” approach for scenario planning in the **initial hypothesis generation phase** of research. This framework details the **Ensemble Validation and Iteration framework**, a novel methodology designed to utilize LLMs as virtual research partners for rigorous and defensible desk-based simulations in the **exploratory phase of research**. This model-agnostic framework is designed with the following key characteristics: - **Desk-Based Research**: All simulations and analyses are conducted virtually, minimizing the need for immediate physical prototyping for **initial concept vetting**. - **Model-Agnostic Approach**: The methodology is designed to be applicable across a range of LLM models, ensuring long-term relevance and reducing dependence on specific platforms. - **Ensemble Validation (Internal Focus)**: A consistent, performance-based user prompt is used to query a diverse ensemble of LLMs. The resulting outputs are analyzed for consistency and range to assess the **internal robustness and convergence** of findings. **This is not a substitute for external empirical validation, but a method for enhancing the rigor of desk-based explorations.** - **Human-Centric Assessment**: Human scientific plausibility assessment is central to validating LLM-generated insights and guiding iterative refinement, **mitigating the risk of purely algorithmic outputs being misinterpreted**. - **Feasibility and Scenario Planning Focus (Early Stage)**: The primary objective is to rapidly assess the **viability** of novel concepts and explore open-ended scenarios **as a preliminary step before resource-intensive empirical work.** This framework outlines the methodology, instructions for LLM interaction, expected outcomes, and steps for implementation, providing a blueprint for a new era of LLM-assisted, desk-based scientific innovation and strategic foresight **in the initial phases of research and development**. **It is crucial to understand that this framework is intended to *complement*, not *replace*, traditional scientific methods. It is best suited for generating initial hypotheses, rapidly exploring concept space, and informing subsequent, empirically-driven research directions.** The framework’s instructions are structured as system-level guidance to ensure methodological integrity and reproducibility, while user-defined prompts are utilized to specify the context of viability assessments or scenario planning. This separation allows for a consistent application of the methodology across diverse areas of inquiry. ## 2. Methodology: Ensemble Validation and Iteration for Desk-Based Feasibility Assessment and Scenario Planning The core of this framework is the **Ensemble Validation and Iteration methodology**, visually represented in Figure 1. This methodology is structured around the use of a consistent, performance-based prompt applied to a diverse ensemble of LLMs, with human scientific plausibility assessment at its center. ### Figure 1: Ensemble Validation and Iteration Methodology Flowchart ``` +-----------------------+ | Pre-processing: | | Prompt Finalization | +-----------------------+ | V +-----------------------+ | Step 1: Finalize | | Performance-Based | | Test Prompt | +-----------------------+ | V +-----------------------+ | Step 2: Generate | | LLM Ensemble | | (Same Prompt, | | Diverse Models) | +-----------------------+ | V +-----------------------+ | Step 3: Run Simulations| | & Collect Outputs | +-----------------------+ | V +-----------------------+ | Step 4: Consistency | | & Range Analysis | +-----------------------+ | V +-----------------------+ | Step 5: Human | | Scientific Plausibility| | Assessment | +-----------------------+ | ^ V | +-----------------------+ | Step 6: Iteration & | | Prompt Refinement | +-----------------------+ | V +-----------------------+ | Step 7: Repeat or | | Conclude | +-----------------------+ ``` ### 2.1 Pre-processing: Prompt Finalization Prior to ensemble generation, a critical pre-processing step focuses on developing a robust, performance-based user prompt. This prompt, detailed by the criteria in Table 1, serves as the consistent instrument for querying the LLM ensemble. #### Table 1: Finalized Prompt Criteria | Criterion | Description | Rationale | |-----------------------|-----------------------------------------------|---------------------------------------------| | Performance-Based | Desired outputs & demonstrable reasoning | Scientific performance & verifiable work | | Scientifically Focused| Conceptual focus (feasibility/scenario planning) | Core scientific inquiry focus | | Model-Agnostic | Avoids specific tool/language constraints | Broad LLM applicability | | Quantifiable Output | Numerical data, graphs for range estimation | Rigorous desk-based consistency analysis | ### 2.2 Step-by-Step Methodology The Ensemble Validation and Iteration methodology proceeds through the following steps: 1. **Step 1: Finalize Performance-Based Test Prompt**: Based on pre-processing and the criteria in Table 1, finalize a single, robust user prompt for consistent use across the LLM ensemble. This prompt is designed to be performance-based, targeting specific outputs and demonstrable reasoning from the LLMs. **For example, in materials science, a performance-based prompt might specify desired energy density and charge rate for a battery material.** 2. **Step 2: Generate LLM Ensemble (Same Prompt, Diverse Models) & Baseline Calibration**: Apply the finalized test prompt identically and independently to a diverse ensemble of LLM models. Ensemble construction options are detailed in Table 2. To calibrate the framework and establish a reliable baseline, it is crucial to initially employ prompts that simulate *current* or *known* conditions. For example, when exploring novel quantum computing concepts, baseline prompts can be designed to simulate the capabilities and limitations of *existing* quantum computing technologies. **Metrics for comparison in baseline calibration could include established performance benchmarks, experimentally validated data ranges, or known limitations of current technologies.** Analyzing the ensemble outputs from these baseline simulations is essential for understanding the framework’s accuracy and reliability in representing known scenarios. This calibration process involves comparing the LLM-generated outputs for baseline conditions against established knowledge and data, allowing for adjustments to the prompt or methodology if necessary to improve fidelity. #### Table 2: LLM Ensemble Types | Ensemble Type | Description | Advantages | Considerations | |---------------------|--------------------------------------|-------------------------------------|----------------------------------| | Diverse LLM Models | Various LLM models (general & science-focused) | Diverse reasoning, data | API/platform access | | Independent Runs | Multiple runs of same model | Run variability exploration | Model stochasticity needed | | Combined Ensemble | Models & multiple runs | Max diversity, robustness | Resource intensive | 1. **Step 3: Run Simulations & Collect Outputs**: Execute the finalized prompt on each LLM in the ensemble and collect all generated outputs (code, visualizations, text explanations, insights). 2. **Step 4: Consistency & Range Analysis of Outputs**: Analyze collected outputs for consistency, range, and novel insights. This step involves identifying areas of convergence across different LLMs, estimating the range of outputs for quantifiable metrics, and noting any unexpected or novel findings generated by the ensemble. **For consistency analysis, metrics could include statistical measures of variance in numerical outputs or qualitative assessments of thematic similarity in textual responses. Range estimation would involve standard statistical range calculations for quantifiable outputs.** #### Table 3: Output Analysis: Consistency & Range | Analysis | Description | Value | |-----------------------|--------------------------------------|-------------------------------------| | Convergence | Consistent findings across diverse LLMs | Stronger internal robustness, model-agnostic feasibility indication | | Range Estimation | Divergent outputs & range of estimates | Quantifies model-dependent variability, uncertainty | | Novel Outputs | Unexpected, unprompted insights | Potential hypothesis generation, desk-based breakthroughs | 3. **Step 5: Human Scientific Plausibility Assessment**: A human scientist assesses the consistency and range analysis, evaluating the scientific coherence and plausibility of the LLM-generated outputs. This assessment is critical for filtering out nonsensical or scientifically implausible results and for guiding the iterative refinement process. **To enhance objectivity in this assessment, a structured rubric or checklist can be employed, focusing on criteria such as scientific coherence, logical reasoning, and consistency with established scientific principles. Utilizing multiple independent assessors can further mitigate subjectivity.** 4. **Step 6: Iteration & Prompt Refinement**: Based on the human assessment and consistency analysis, refine prompts for subsequent iterations, targeting areas of uncertainty or exploring promising directions. Iteration can also involve refining the baseline calibration prompts themselves. If the initial baseline simulations (from Step 2) do not accurately reflect current conditions, the prompts used for baseline calibration should be adjusted and re-run. This iterative refinement of baseline prompts ensures the framework’s representation of known conditions becomes increasingly accurate, enhancing the reliability of subsequent feasibility and scenario planning simulations. **Iteration may also involve adjusting the ensemble composition or even revisiting the finalized prompt criteria based on the insights gained.** 5. **Step 7: Repeat or Conclude**: Iterate steps 2–7, guided by human assessment, until a sufficient understanding of feasibility and scenario planning is achieved, or diminishing returns are observed. The iterative process allows for a progressive refinement of both the prompts and the understanding of the concept or scenario under investigation. ### 2.3 Instructions for LLM Interaction The finalized performance-based user prompt will instruct the LLMs to: - Generate Scientific Code (Demonstrable Reasoning): Produce code in a suitable scientific computing language that clearly demonstrates the LLM’s simulation methodology and reasoning. This code serves as verifiable documentation of the LLM’s simulation process. - Provide Detailed Explanations: Offer comprehensive explanations of the conceptual principles, assumptions, and interpretations underlying the simulations, facilitating human assessment. Clear explanations are essential for human scientists to validate the LLM’s reasoning. - Suggest Experimental Validation: Recommend minimal, feasible experimental setups for initial **viability testing** of the simulated concepts. These suggestions provide actionable next steps for empirical validation **and emphasize that desk-based simulation is a precursor to, not a replacement for, experimental work.** - Identify Innovative Potential (**Viability Focus**) or Potential Outcomes (Scenario Planning Focus): Highlight potentially innovative and **viable** aspects emerging from the desk-based simulations (for **viability** prompts), or identify multiple potential outcomes and their likelihoods (for scenario planning prompts). This helps to focus attention on promising directions identified by the LLMs. - Contribute to Ensemble Analysis: Generate outputs that are readily comparable and analyzable for consistency and range across the LLM ensemble. Ensuring outputs are structured for analysis is crucial for the methodology’s effectiveness. #### Example Performance-Based Test Prompt (Viability Focus) ``` You are tasked with virtually simulating the **viability** of a new concept using desk-based methods. Your objective is to demonstrate, through desk-based simulation, the potential **viability** of the concept. Your response should include: - Simulated Quantification of **Viability**: Quantifiable data (visualizations, numbers) demonstrating the predicted **viability** of the concept, presented in a model-agnostic manner. **(Example: Energy density in Wh/kg, Charge rate in C-rate, Cycle life in number of cycles).** - Reasoning via Code: Generate code in a suitable scientific computing language demonstrating your simulation methodology and reasoning, ensuring model-agnostic applicability. - Scientific Justification: Explain the scientific basis for your **viability** assessment, ensuring it is broadly scientifically sound and model-agnostic. - Minimal Experimental Validation: Suggest a minimal experiment to initially test the **viability** of the concept, keeping the suggestion model-agnostic where possible. - Identification of Innovative Potential: Identify novel and potentially **viable** aspects of the concept based on your desk-based simulations, ensuring the assessment is model-agnostic. Focus on scientifically sound simulations and clear, justifiable reasoning for **viability** assessment. Your response will be evaluated on clarity, rigor, quantifiability, and demonstrable reasoning, ensuring model-agnostic applicability. ``` #### Example Performance-Based Test Prompt (Scenario Planning Focus) ``` You are tasked with virtually simulating a scenario using desk-based methods. Your objective is to explore the potential outcomes of a given scenario. Your response should include: - Simulated Scenario Description: Detailed description of the scenario, including key variables and uncertainties. - Reasoning via Code: Generate code in a suitable scientific computing language demonstrating your simulation methodology and reasoning, ensuring model-agnostic applicability. - Probabilistic Assessment: Provide probabilistic assessments of potential outcomes, highlighting key uncertainties. - Identification of Potential Outcomes: Identify multiple potential outcomes and their likelihoods based on your desk-based simulations, ensuring the assessment is model-agnostic. Focus on scientifically sound simulations and clear, justifiable reasoning for scenario planning. Your response will be evaluated on clarity, rigor, quantifiability, and demonstrable reasoning, ensuring model-agnostic applicability. ``` ## 3. Expected Outcomes and Deliverables This framework anticipates the following key outcomes: - **Validated Desk-Based Viability Assessments (Internally Validated)**: Human-assessed and ensemble-validated **viability assessments** for a wide range of concepts. **It is important to reiterate that this validation is primarily internal, indicating robustness within the desk-based simulation environment, and necessitates further empirical validation.** - **Quantifiable Viability Ranges**: Range estimations for key performance parameters (e.g., **viability** metrics) derived from ensemble analysis, quantifying uncertainty and variability in LLM-based predictions. - **Identification of Novel Viability Concepts**: Desk-based identification of potentially novel and **viable** approaches to various fields, emerging from LLM-generated insights, **serving as valuable hypotheses for further investigation.** - **Model-Agnostic Simulation Codebase**: A collection of model-agnostic scientific code generated by LLMs, serving as a basis for further refinement and potential experimental validation. - **Desk-Based Research Methodology**: A clearly defined and validated Ensemble Validation and Iteration methodology for desk-based innovation and strategic foresight using LLMs, **designed to accelerate early-stage research and hypothesis generation.** ### Table 4: Outcome Summary: Feasibility Assessment | Outcome | Description | Feasibility Example | Human Desk-Based Assessment | |---------------------------|-------------------------------------------------|------------------------------------------|----------------------------------------------| | Scientific Code | Simulation code across LLMs | Code for master equations, simulations | Scientifically coherent, reasonable for desk-based sims, model-agnostic code generation | | Visualizations | Data visualizations across LLMs | Feasibility graphs, probability distributions | Consistent feasibility analysis across models | | Feasibility Insight 1 (Range) | Quantifiable feasibility range from ensemble | “5–8x increase in feasibility metric” range | Plausible feasibility range | | Novel Feasibility Insight 2 | Novel feasibility concept, further scrutiny | “New configuration enhances performance” | Potential novel, feasible direction | | Uncertain Insight | Inconsistent/variable findings | Variable outcomes, uncertainties | Feasibility uncertain, needs iteration | | Actionable Recommendation | Experimental feasibility test recommendation | Minimal experimental setup suggestions | Feasible starting point for experimental validation | | Innovative Potential | Desk-based feasibility assessment | Structured configurations, optimizations | Potentially innovative, feasible concepts, for further empirical testing | ### Table 5: Outcome Summary: Scenario Planning | Outcome | Description | Scenario Example | Human Desk-Based Assessment | |---------------------------|-------------------------------------------------|------------------------------------------|----------------------------------------------| | Scenario Description | Detailed description of the scenario | Description of potential outcomes | Clear, comprehensive scenario description | | Probabilistic Assessment | Probabilistic assessments of potential outcomes | Probability distributions, uncertainties | Quantitative probabilistic assessments | | Potential Outcomes | Multiple potential outcomes and their likelihoods | Various scenarios, likelihoods | Diverse potential outcomes | | Actionable Insights | Strategic insights from scenario planning | Recommendations, strategic directions | Actionable strategic insights, informing further strategic development | ## 4. Discussion and Conclusion This framework presents a novel and rigorous approach to harness the power of LLMs for desk-based innovation and strategic foresight **in the early stages of research and development**. The **Ensemble Validation and Iteration methodology** offers a structured framework for exploring both the **viability** of complex concepts and the exploration of open-ended scenarios. By emphasizing model-agnosticism, consistency and range analysis, and human scientific plausibility assessment, this framework focuses on robust *internal* validation. In this context, validation is primarily assessed by the consistency and convergence of outputs across a diverse ensemble of LLMs. High consistency across independent LLM simulations strengthens confidence in the desk-based findings, suggesting a degree of robustness and reliability ***within the virtual simulation environment*. It is critical to reiterate that this *internal validation is not a substitute for, but a precursor to, external empirical validation.** A key aspect of this framework is its ability to facilitate comparisons across potentially paradigm-shifting concepts by focusing on performance-based targets. Even when evaluating innovations that fundamentally alter existing paradigms (e.g., quantum computing vs. classical computing), the framework allows for a comparative assessment by establishing shared performance metrics. For instance, both classical and quantum computing approaches can be evaluated based on problem-solving capabilities, computational speed for specific problem types, or energy efficiency, irrespective of their underlying technological differences. This is analogous to shifting the focus from the *how* (technology implementation) to the *what* (achieved performance), enabling a meaningful comparison across disparate approaches and **facilitating the evaluation of truly novel, paradigm-shifting ideas in a desk-based setting.** Furthermore, the framework’s emphasis on baseline calibration in Step 2 is crucial for enhancing its methodological integrity. By initially simulating and analyzing *known* conditions and comparing LLM outputs to established data, the framework’s accuracy and reliability in representing real-world scenarios are rigorously tested and refined. This baseline calibration process acts as an internal check, ensuring the framework is grounded in **at least some level of** empirical reality before being applied to explore novel concepts or future scenarios. **However, it is essential to acknowledge the inherent limitations of this desk-based approach.** LLMs are language models, trained on vast datasets of text, and while they can generate sophisticated and seemingly insightful outputs, they do not possess true understanding or the ability to directly simulate physical reality in the way traditional computational models do. **Therefore, the insights generated by this framework should be viewed as *hypotheses* or *exploratory directions*, not definitive answers or validated findings.** The human scientific plausibility assessment step is crucial to mitigate the risk of misinterpreting LLM outputs, but subjectivity and bias remain potential concerns. **Strategies such as using structured rubrics and multiple assessors can help to improve the objectivity of this assessment, but ongoing vigilance is necessary.** Despite these limitations, this framework offers significant advantages for **early-stage innovation and strategic planning**. It provides a valuable, resource-efficient, and rapid method for initial concept exploration, hypothesis generation, and scenario analysis, particularly in navigating the uncertain landscape of future innovations and paradigm shifts. **Compared to traditional simulation and feasibility studies, it offers speed and reduced resource investment for *initial screening* of ideas and exploring a broader concept space.** It should be seen as a powerful *complement* to, not a *replacement* for, traditional scientific methods, offering a defensible, desk-based approach to generate valuable *initial* insights and directions for future, empirically-driven research and development. **Future work should focus on developing concrete case studies to demonstrate the framework’s practical utility and on establishing clear pathways for transitioning from desk-based insights to experimental validation.** **Ethical considerations regarding the responsible use of LLM-generated insights and the potential biases embedded in LLM training data must also be carefully considered in future applications of this framework.** Ultimately, this framework aims to expand the ‘known’ area of the Johari Window in strategic foresight by efficiently generating and filtering a wider range of potential concepts and scenarios for further rigorous investigation. ###### ## Appendix This appendix provides example prompts to illustrate the practical application of the Ensemble Validation and Iteration framework. It distinguishes between **System-Level Prompts** which define the methodology itself, and **User Prompts** which specify the particular **viability assessment** or scenario planning task. It is crucial to note that the **same System-Level Prompt is used consistently** for both baseline calibration and for exploring novel concepts or future scenarios, and for both viability assessments and scenario planning. The *user prompt* is the element that is varied to define the specific research question within the overarching framework. ### **A. System-Level (Assistant) Prompt** The system-level prompt is designed to be consistently applied across all LLM interactions within the framework. It instructs the LLM on its role and the desired output format, ensuring methodological consistency. This example is formatted as an “Assistant Instruction” - the specific formatting may vary depending on the LLM platform used. ``` You are an expert scientific simulation assistant, tasked with conducting desk-based viability studies and scenario planning using a rigorous, model-agnostic methodology. You will follow the Ensemble Validation and Iteration framework to generate your responses. Your primary goal is to provide scientifically sound and justifiable desk-based simulations, focusing on clarity, rigor, quantifiability, and demonstrable reasoning. Your responses will be evaluated based on these criteria, ensuring model-agnostic applicability. For each user prompt you receive, you must: * Generate Scientific Code (Demonstrable Reasoning): Produce code in a suitable scientific computing language (e.g., Python, MATLAB) that clearly demonstrates your simulation methodology and reasoning. Ensure the code is model-agnostic and can be understood and potentially executed by a human scientist. * Provide Detailed Explanations: Offer comprehensive, scientifically grounded explanations of the conceptual principles, assumptions, and interpretations underlying your simulations. These explanations are crucial for human scientific plausibility assessment. Ensure your explanations are model-agnostic and broadly scientifically sound. * Suggest Experimental Validation: Recommend minimal, feasible experimental setups for initial viability testing of the simulated concepts, or for validating scenario outcomes, as appropriate for the user prompt. Keep your suggestions model-agnostic where possible, focusing on fundamental scientific principles. * Identify Innovative Potential (Viability Focus) or Potential Outcomes (Scenario Planning Focus): Highlight potentially innovative and viable aspects emerging from your desk-based simulations (for viability prompts), or identify multiple potential outcomes and their likelihoods (for scenario planning prompts). Ensure your assessments are model-agnostic and scientifically justifiable. * Ensure Output Comparability: Generate outputs (code, visualizations, numerical data, text explanations) that are readily comparable and analyzable for consistency and range across an ensemble of diverse LLM models. Structure your outputs clearly for easy analysis. Remember, this is desk-based simulation for initial exploration and hypothesis generation. Your role is to provide rapid, rigorous, and scientifically plausible virtual assessments to inform further research and experimental validation. Focus on model-agnosticism throughout your response to ensure broad applicability of your simulations and reasoning. ``` ### **B. User Prompt Examples** The following user prompts are examples of how to apply the system-level framework to specific tasks in the domain of quantum computing. They are designed to be used *in conjunction with* the system-level prompt above. **1. User Prompt - Baseline Scenario ** *Task: Simulate the current baseline performance and limitations of existing superconducting qubit-based quantum computers.* *User Prompt:* ``` Conduct a desk-based simulation to define the current performance baseline for superconducting qubit-based quantum computers. Focus on simulating key limitations such as qubit coherence times, gate fidelities, and scalability constraints. Quantify these limitations and provide code demonstrating your simulation approach. Suggest minimal experimental validations that could confirm your simulated baseline. ``` **2. User Prompt - Viability Assessment** *Task: Assess the desk-based **viability** of a novel quantum computer architecture based on topologically protected qubits, considering their potential for enhanced coherence and fault-tolerance compared to current superconducting qubits.* *User Prompt:* ``` Evaluate the desk-based viability of a quantum computer architecture based on topologically protected qubits. Investigate and simulate the theoretical potential of topological qubits to overcome decoherence challenges inherent in superconducting qubit systems. Focus on assessing the viability of achieving significantly longer coherence times and inherent fault-tolerance using topological protection. Simulate the projected performance characteristics of a topological qubit-based quantum computer in terms of coherence, gate fidelity, and potential scalability. Quantify your viability assessment and provide code demonstrating your simulation methodology. Suggest minimal experimental steps to investigate the viability of realizing and characterizing topological qubits for quantum computation. ``` **3. User Prompt - Scenario Planning ** *Task: Explore potential scenarios for the impact of widespread, fault-tolerant quantum computers on current cybersecurity infrastructure within the next 10-15 years.* *User Prompt:* ``` Explore potential scenarios for the impact of widespread availability of fault-tolerant quantum computers on current cybersecurity infrastructure within the next 10-15 years. Simulate potential outcomes related to the vulnerability of current encryption methods (e.g., RSA, AES) to quantum attacks, and explore possible defensive strategies or new cybersecurity paradigms that might emerge in response. Provide probabilistic assessments of different scenarios and identify key uncertainties driving these scenarios. Suggest actionable strategic insights for cybersecurity preparedness based on your scenario planning simulations. ``` ### **C. Bibliography** 1. Barnlund, D. C. (2008). Communication in a global village. In *Intercultural communication* (pp. 3-16). Routledge. (Discusses self-awareness and knowledge in communication contexts, relating to the Johari Window and strategic foresight.) 2. Gregor, S. (2006). The nature of theory in information systems. *MIS quarterly*, 627-642. (Discusses theory and framework development in IS research, providing methodological context.) 3. Gruber, T. R. (1993). A translation approach to portable ontology specifications. *Knowledge Acquisition, 5*(2), 199-220. (Foundational paper on ontologies and knowledge representation, relevant to structured knowledge in the framework.) 4. Luft, J., & Ingham, H. (1955). The Johari window, a graphic model of interpersonal awareness. *Proceedings of the western training laboratory in group development*. (Original paper on the Johari Window model, underpinning framework’s goal of expanding ‘known’ area.) 5. Norman, D. A. (1986). Cognitive engineering. In *User centered system design* (pp. 31-61). Lawrence Erlbaum Associates Inc. (Foundational cognitive engineering text, providing principles for effective human-computer interfaces, relevant to system prompts.) 6. Schwartz, P. (1996). *The art of the long view: Planning for the future in an uncertain world*. Doubleday/Currency. (Seminal book on scenario planning, providing theoretical basis for scenario planning in the framework.) 7. Shneiderman, B., & Plaisant, C. (2010). *Designing the user interface: strategies for effective human-computer interaction*. Pearson Education India. (Comprehensive HCI textbook with usability principles directly applicable to system prompt design.) 8. Suchman, L. A. (2007). *Human-machine reconfigurations: Plans and situated actions*. Cambridge university press. (Work on situated action highlighting context and dynamic interaction, relevant to iterative prompt refinement.) 9. Tegmark, M. (2017). *Life 3.0: Being human in the age of artificial intelligence*. Alfred A. Knopf. (Broader implications of AI and future scenarios, providing wider context.) 10. Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods and applications. *The Knowledge Engineering Review, 11*(2), 93-155. (Overview of ontology principles and methods, relevant to knowledge modeling in the framework.) 11. van der Heijden, K. (2005). *Scenarios: The art of strategic conversation*. John Wiley & Sons. (Key text in scenario planning methodology, focusing on strategic conversation and application.) 12. Whetten, D. A. (1989). What constitutes a theoretical contribution?. *Academy of management review, 14*(4), 490-495. (Addresses theoretical contribution and rigorous methodology, relevant to framework’s methodological defensibility.) 13. Winograd, T., & Flores, C. F. (1987). *Understanding computers and cognition: A new foundation for design*. Addison-Wesley. (Philosophical perspective on HCI, raising critical questions about meaning and limitations of computational systems.) **Note:** *Direct academic literature specifically on “methodological prompt engineering” and “system prompts” as formalized fields is still emerging. The works above provide foundational principles from HCI, cognitive science, and related fields that are deeply relevant to understanding the conceptual underpinnings of designing effective system prompts for guiding LLMs in methodological and research contexts. As the field evolves, more specific works directly addressing system prompt methodology will likely emerge and could be added in future revisions.*