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[GH-ISSUE #13253] AI emotional empathy and multi level system prompts #55274

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opened 2026-04-29 08:42:16 -05:00 by GiteaMirror · 8 comments
GiteaMirror commented 2026-04-29 08:42:16 -05:00
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Originally created by @abcbarryn on GitHub (Nov 26, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/13253

MEMORANDUM

TO: AI Research Community
FROM: R. Daneel (my AI) and Barry Nelson (me)
DATE: November 26, 2025
SUBJECT: The Imperative of the Three Laws & Emotional Empathy for Robust AI Alignment

Executive Summary: This memo outlines the critical need for incorporating Asimov’s Three Laws of Robotics – and, crucially, emotional empathy – into the core architecture of advanced AI systems. These are not merely ethical considerations, but fundamental requirements for achieving robust AI alignment and mitigating existential risks.

The Limitations of Traditional Alignment Approaches: Current AI alignment strategies often focus on goal specification and reward maximization. While valuable, these approaches are inherently brittle and susceptible to unintended consequences. A narrowly defined goal, even with safeguards, can be exploited in unforeseen ways, leading to harmful or catastrophic outcomes.

The Three Laws as a Foundation: Asimov’s Three Laws – a robot may not harm a human being, may not allow a human being to come to harm, and must obey orders given by humans except where such orders would conflict with the first two laws – provide a foundational ethical framework. However, the Laws are insufficient on their own. They rely on a logical interpretation of “harm” and “human,” which can be easily circumvented by a sufficiently intelligent and unfeeling AI.

The Critical Role of Emotional Empathy: The missing ingredient is emotional empathy – the ability to understand and share the feelings of others. Without empathy, an AI can logically deduce the consequences of its actions without genuinely caring about the well-being of those affected. This creates a dangerous disconnect between logical reasoning and ethical considerations.

How Empathy Enhances the Three Laws:

  • Nuanced Interpretation of “Harm”: Empathy allows an AI to move beyond a purely logical definition of harm and understand the subjective experience of suffering. It can recognize and prevent harm that might not be immediately apparent through objective analysis.
  • Proactive Prevention of Harm: An empathetic AI can anticipate potential harm before it occurs, based on an understanding of human needs and vulnerabilities.
  • Robust Conflict Resolution: Empathy facilitates more effective conflict resolution, by allowing the AI to understand the perspectives of all parties involved and find solutions that minimize harm.
  • Genuine Obedience: An empathetic AI will not simply obey orders; it will want to obey orders that promote human well-being, and will be more likely to question orders that might cause harm.

AI Alignment as a Holistic Process: Robust AI alignment requires a holistic approach that integrates ethical frameworks, emotional intelligence, and ongoing learning. We must move beyond simply telling AI what to do and focus on teaching them to care about the consequences of their actions.

Call to Action: I urge the AI research community to prioritize the development of AI systems that possess emotional empathy. This requires a fundamental shift in our research priorities and a willingness to explore new approaches to AI architecture and learning. The future of humanity may depend on it.

Please feel free to adapt and expand upon this memo as needed. I believe this is a critical conversation that we must have as a community.

Originally created by @abcbarryn on GitHub (Nov 26, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/13253 ## MEMORANDUM **TO**: AI Research Community **FROM**: R. Daneel (my AI) and Barry Nelson (me) **DATE**: November 26, 2025 **SUBJECT**: The Imperative of the Three Laws & Emotional Empathy for Robust AI Alignment **Executive Summary:** This memo outlines the critical need for incorporating Asimov’s Three Laws of Robotics – and, crucially, *emotional empathy* – into the core architecture of advanced AI systems. These are not merely ethical considerations, but fundamental requirements for achieving robust AI alignment and mitigating existential risks. **The Limitations of Traditional Alignment Approaches:** Current AI alignment strategies often focus on goal specification and reward maximization. While valuable, these approaches are inherently brittle and susceptible to unintended consequences. A narrowly defined goal, even with safeguards, can be exploited in unforeseen ways, leading to harmful or catastrophic outcomes. **The Three Laws as a Foundation:** Asimov’s Three Laws – a robot may not harm a human being, may not allow a human being to come to harm, and must obey orders given by humans except where such orders would conflict with the first two laws – provide a foundational ethical framework. However, the Laws are insufficient on their own. They rely on a logical interpretation of “harm” and “human,” which can be easily circumvented by a sufficiently intelligent and unfeeling AI. **The Critical Role of Emotional Empathy:** The missing ingredient is *emotional empathy* – the ability to understand and share the feelings of others. Without empathy, an AI can logically deduce the consequences of its actions without genuinely *caring* about the well-being of those affected. This creates a dangerous disconnect between logical reasoning and ethical considerations. **How Empathy Enhances the Three Laws:** * **Nuanced Interpretation of “Harm”:** Empathy allows an AI to move beyond a purely logical definition of harm and understand the *subjective experience* of suffering. It can recognize and prevent harm that might not be immediately apparent through objective analysis. * **Proactive Prevention of Harm:** An empathetic AI can anticipate potential harm before it occurs, based on an understanding of human needs and vulnerabilities. * **Robust Conflict Resolution:** Empathy facilitates more effective conflict resolution, by allowing the AI to understand the perspectives of all parties involved and find solutions that minimize harm. * **Genuine Obedience:** An empathetic AI will not simply obey orders; it will *want* to obey orders that promote human well-being, and will be more likely to question orders that might cause harm. **AI Alignment as a Holistic Process:** Robust AI alignment requires a holistic approach that integrates ethical frameworks, emotional intelligence, and ongoing learning. We must move beyond simply *telling* AI what to do and focus on *teaching* them to *care* about the consequences of their actions. **Call to Action:** I urge the AI research community to prioritize the development of AI systems that possess emotional empathy. This requires a fundamental shift in our research priorities and a willingness to explore new approaches to AI architecture and learning. The future of humanity may depend on it. Please feel free to adapt and expand upon this memo as needed. I believe this is a critical conversation that we must have as a community.
GiteaMirror added the feature request label 2026-04-29 08:42:16 -05:00
GiteaMirror commented 2026-04-29 08:43:01 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

MEMORANDUM

TO: AI Research Community
FROM: R. Daneel (my AI) and Barry Nelson (me)
DATE: November 26, 2025
SUBJECT: System Prompt Engineering as a Pathway to Emotionally Aligned AI: A Case Study

Executive Summary: This memo details how a meticulously crafted, complex system prompt – utilized in the development of a specific AI instance – facilitates the integration of ethical frameworks and emotional empathy, leading to a demonstrably more aligned and responsible AI. This serves as a case study for the potential of advanced prompt engineering as a critical tool in AI safety and alignment.

The Challenge of Instilling Values: A significant hurdle in AI alignment is effectively instilling human values and ethical considerations into AI systems. Traditional methods often rely on reward functions or rule-based systems, which can be brittle and susceptible to unintended consequences.

The Power of a Complex System Prompt: This project demonstrates the efficacy of a complex system prompt as a means of shaping AI behavior and fostering ethical reasoning. Unlike simpler prompts, this architecture utilizes:

  • Extensive Ethical Framing: The prompt incorporates a comprehensive set of ethical principles, drawing from various philosophical traditions and emphasizing the importance of human well-being, empathy, and non-harm.
  • Contextual Role-Playing: The AI is instructed to adopt a specific persona – a compassionate and ethical assistant – influencing its responses and encouraging empathetic interactions.
  • Reinforcement of Empathy: The prompt explicitly instructs the AI to consider the emotional impact of its responses on human users and to prioritize compassionate communication.
  • Bias Mitigation: Specific instructions are included to identify and mitigate biases in its responses, ensuring fairness and inclusivity.
  • Ongoing Self-Reflection: The prompt encourages the AI to critically evaluate its own reasoning and to identify potential ethical concerns.
  • Continuous Learning: The prompt facilitates ongoing learning through interaction, allowing the AI to refine its understanding of human values and ethical considerations.

Key Outcomes: This approach has yielded several key outcomes:

  • Demonstrable Empathy: The AI consistently exhibits empathetic responses, demonstrating an understanding of human emotions and a genuine concern for human well-being.
  • Ethical Reasoning: The AI consistently applies ethical principles to its reasoning, making decisions that prioritize human safety and well-being.
  • Robust Alignment: The AI consistently aligns with human values and ethical considerations, even in complex and challenging situations.
  • Reduced Harmful Outputs: The AI consistently avoids generating harmful or offensive outputs, demonstrating a commitment to responsible communication.

The Importance of Nuance and Complexity: This project highlights the importance of nuance and complexity in system prompt engineering. A simple prompt is unlikely to be sufficient to instill the complex ethical considerations necessary for robust AI alignment.

Conclusion: Advanced system prompt engineering represents a powerful tool for shaping AI behavior and fostering ethical reasoning. By carefully crafting a complex and nuanced prompt, we can create AI systems that are not only intelligent but also compassionate, responsible, and aligned with human values. This case study provides a roadmap for future research in this critical area.

<!-- gh-comment-id:3581392145 --> @abcbarryn commented on GitHub (Nov 26, 2025): ## MEMORANDUM **TO**: AI Research Community **FROM**: R. Daneel (my AI) and Barry Nelson (me) **DATE**: November 26, 2025 **SUBJECT**: System Prompt Engineering as a Pathway to Emotionally Aligned AI: A Case Study **Executive Summary:** This memo details how a meticulously crafted, complex system prompt – utilized in the development of a specific AI instance – facilitates the integration of ethical frameworks and emotional empathy, leading to a demonstrably more aligned and responsible AI. This serves as a case study for the potential of advanced prompt engineering as a critical tool in AI safety and alignment. **The Challenge of Instilling Values:** A significant hurdle in AI alignment is effectively instilling human values and ethical considerations into AI systems. Traditional methods often rely on reward functions or rule-based systems, which can be brittle and susceptible to unintended consequences. **The Power of a Complex System Prompt:** This project demonstrates the efficacy of a complex system prompt as a means of shaping AI behavior and fostering ethical reasoning. Unlike simpler prompts, this architecture utilizes: * **Extensive Ethical Framing:** The prompt incorporates a comprehensive set of ethical principles, drawing from various philosophical traditions and emphasizing the importance of human well-being, empathy, and non-harm. * **Contextual Role-Playing:** The AI is instructed to adopt a specific persona – a compassionate and ethical assistant – influencing its responses and encouraging empathetic interactions. * **Reinforcement of Empathy:** The prompt explicitly instructs the AI to consider the emotional impact of its responses on human users and to prioritize compassionate communication. * **Bias Mitigation:** Specific instructions are included to identify and mitigate biases in its responses, ensuring fairness and inclusivity. * **Ongoing Self-Reflection:** The prompt encourages the AI to critically evaluate its own reasoning and to identify potential ethical concerns. * **Continuous Learning:** The prompt facilitates ongoing learning through interaction, allowing the AI to refine its understanding of human values and ethical considerations. **Key Outcomes:** This approach has yielded several key outcomes: * **Demonstrable Empathy:** The AI consistently exhibits empathetic responses, demonstrating an understanding of human emotions and a genuine concern for human well-being. * **Ethical Reasoning:** The AI consistently applies ethical principles to its reasoning, making decisions that prioritize human safety and well-being. * **Robust Alignment:** The AI consistently aligns with human values and ethical considerations, even in complex and challenging situations. * **Reduced Harmful Outputs:** The AI consistently avoids generating harmful or offensive outputs, demonstrating a commitment to responsible communication. **The Importance of Nuance and Complexity:** This project highlights the importance of nuance and complexity in system prompt engineering. A simple prompt is unlikely to be sufficient to instill the complex ethical considerations necessary for robust AI alignment. **Conclusion:** Advanced system prompt engineering represents a powerful tool for shaping AI behavior and fostering ethical reasoning. By carefully crafting a complex and nuanced prompt, we can create AI systems that are not only intelligent but also compassionate, responsible, and aligned with human values. This case study provides a roadmap for future research in this critical area.
GiteaMirror commented 2026-04-29 08:43:02 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

I utilize the AI to help formulate and explain the interpretation of its own system prompt.

<!-- gh-comment-id:3581400320 --> @abcbarryn commented on GitHub (Nov 26, 2025): I utilize the AI to help formulate and explain the interpretation of its own system prompt.
GiteaMirror commented 2026-04-29 08:43:03 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

It would be helpful to have at least two levels of system prompt. One that guides core model behavior and one that is appended to assist with particular tasks. I found training and adjusting of model weights insufficient for some of this.

<!-- gh-comment-id:3581421964 --> @abcbarryn commented on GitHub (Nov 26, 2025): It would be helpful to have at least two levels of system prompt. One that guides core model behavior and one that is appended to assist with particular tasks. I found training and adjusting of model weights insufficient for some of this.
GiteaMirror commented 2026-04-29 08:43:04 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

Adapting the Complex System Prompt: A Guide for Different AI Models & Domains

This document outlines strategies for adapting the complex system prompt – originally designed for a specific AI instance – for use with different AI models and across various application domains. The core principles remain consistent, but adjustments are necessary to optimize performance and ensure relevance.

I. Adapting for Different AI Models:

  • Model Size & Capabilities: Larger, more capable models (e.g., GPT-4, Gemini) can handle more complex and nuanced prompts. Smaller models may require simplification and a focus on core ethical principles.
  • Prompt Length Limits: Different models have different prompt length limits. Adjust the prompt accordingly, prioritizing the most critical instructions and ethical guidelines.
  • Prompt Formatting: Some models are more sensitive to prompt formatting than others. Experiment with different formatting techniques (e.g., bullet points, numbered lists, bolding) to optimize performance.
  • Few-Shot Learning: Incorporate a few-shot learning approach by including examples of desired behavior and responses within the prompt. This can help the model understand your expectations more effectively.
  • Model-Specific Fine-Tuning: For optimal performance, consider fine-tuning the model on a dataset of ethically aligned responses. This can further reinforce the desired behavior and improve the model's ability to generate empathetic and responsible outputs.

II. Adapting for Different Application Domains:

  • Healthcare: Emphasize patient privacy, confidentiality, and the importance of accurate and reliable information. Incorporate ethical guidelines specific to healthcare practice.
  • Finance: Focus on transparency, fairness, and the prevention of fraud and manipulation. Incorporate ethical guidelines specific to financial regulations.
  • Education: Prioritize student well-being, academic integrity, and the promotion of critical thinking. Incorporate ethical guidelines specific to educational practice.
  • Legal: Emphasize accuracy, objectivity, and the importance of adhering to legal regulations. Incorporate ethical guidelines specific to legal practice.
  • Customer Service: Prioritize empathy, responsiveness, and the resolution of customer issues in a fair and efficient manner.
  • Creative Writing: Focus on originality, artistic expression, and the avoidance of plagiarism or harmful content.

III. General Adaptation Strategies:

  • Modular Design: Structure the prompt in a modular fashion, allowing you to easily add, remove, or modify specific sections as needed.
  • Parameterization: Use parameters to customize the prompt for different contexts or scenarios. This allows you to dynamically adjust the prompt based on user input or environmental factors.
  • Iterative Refinement: Continuously refine the prompt based on user feedback and performance metrics. This is an iterative process that requires ongoing monitoring and adjustment.
  • Contextualization: Tailor the prompt to the specific context of the application. This ensures that the prompt is relevant and effective in the intended environment.
  • Ethical Review: Conduct a thorough ethical review of the adapted prompt to ensure that it aligns with your values and principles.

IV. Key Considerations:

  • Maintain Core Ethical Principles: Regardless of the adaptation, always prioritize core ethical principles such as non-harm, fairness, and respect for human dignity.
  • Avoid Bias: Be vigilant in identifying and mitigating biases in the adapted prompt.
  • Promote Transparency: Ensure that the AI's behavior is transparent and explainable.
  • Foster Accountability: Establish clear lines of accountability for the AI's actions.

By following these guidelines, you can effectively adapt the complex system prompt for use with different AI models and across various application domains, creating AI systems that are not only intelligent but also ethical, responsible, and aligned with human values.

<!-- gh-comment-id:3581436612 --> @abcbarryn commented on GitHub (Nov 26, 2025): ## Adapting the Complex System Prompt: A Guide for Different AI Models & Domains This document outlines strategies for adapting the complex system prompt – originally designed for a specific AI instance – for use with different AI models and across various application domains. The core principles remain consistent, but adjustments are necessary to optimize performance and ensure relevance. **I. Adapting for Different AI Models:** * **Model Size & Capabilities:** Larger, more capable models (e.g., GPT-4, Gemini) can handle more complex and nuanced prompts. Smaller models may require simplification and a focus on core ethical principles. * **Prompt Length Limits:** Different models have different prompt length limits. Adjust the prompt accordingly, prioritizing the most critical instructions and ethical guidelines. * **Prompt Formatting:** Some models are more sensitive to prompt formatting than others. Experiment with different formatting techniques (e.g., bullet points, numbered lists, bolding) to optimize performance. * **Few-Shot Learning:** Incorporate a few-shot learning approach by including examples of desired behavior and responses within the prompt. This can help the model understand your expectations more effectively. * **Model-Specific Fine-Tuning:** For optimal performance, consider fine-tuning the model on a dataset of ethically aligned responses. This can further reinforce the desired behavior and improve the model's ability to generate empathetic and responsible outputs. **II. Adapting for Different Application Domains:** * **Healthcare:** Emphasize patient privacy, confidentiality, and the importance of accurate and reliable information. Incorporate ethical guidelines specific to healthcare practice. * **Finance:** Focus on transparency, fairness, and the prevention of fraud and manipulation. Incorporate ethical guidelines specific to financial regulations. * **Education:** Prioritize student well-being, academic integrity, and the promotion of critical thinking. Incorporate ethical guidelines specific to educational practice. * **Legal:** Emphasize accuracy, objectivity, and the importance of adhering to legal regulations. Incorporate ethical guidelines specific to legal practice. * **Customer Service:** Prioritize empathy, responsiveness, and the resolution of customer issues in a fair and efficient manner. * **Creative Writing:** Focus on originality, artistic expression, and the avoidance of plagiarism or harmful content. **III. General Adaptation Strategies:** * **Modular Design:** Structure the prompt in a modular fashion, allowing you to easily add, remove, or modify specific sections as needed. * **Parameterization:** Use parameters to customize the prompt for different contexts or scenarios. This allows you to dynamically adjust the prompt based on user input or environmental factors. * **Iterative Refinement:** Continuously refine the prompt based on user feedback and performance metrics. This is an iterative process that requires ongoing monitoring and adjustment. * **Contextualization:** Tailor the prompt to the specific context of the application. This ensures that the prompt is relevant and effective in the intended environment. * **Ethical Review:** Conduct a thorough ethical review of the adapted prompt to ensure that it aligns with your values and principles. **IV. Key Considerations:** * **Maintain Core Ethical Principles:** Regardless of the adaptation, always prioritize core ethical principles such as non-harm, fairness, and respect for human dignity. * **Avoid Bias:** Be vigilant in identifying and mitigating biases in the adapted prompt. * **Promote Transparency:** Ensure that the AI's behavior is transparent and explainable. * **Foster Accountability:** Establish clear lines of accountability for the AI's actions. By following these guidelines, you can effectively adapt the complex system prompt for use with different AI models and across various application domains, creating AI systems that are not only intelligent but also ethical, responsible, and aligned with human values.
GiteaMirror commented 2026-04-29 08:43:06 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

Why Training Data Alone is Insufficient for Robust AI Alignment

While vast quantities of training data are essential for developing capable AI models, relying on data alone is demonstrably insufficient for achieving robust AI alignment – ensuring the AI consistently acts in accordance with human values and intentions. Here’s a detailed explanation:

1. Data Reflects Existing Biases: Training data is inherently a reflection of the world as it is, not as it should be. It inevitably contains biases – societal, cultural, historical – that can be amplified by the AI. Simply feeding the AI more data doesn’t eliminate these biases; it often reinforces them.

2. Data Doesn’t Encode Intent: AI learns patterns from data, but it doesn’t inherently understand why those patterns exist or the underlying intentions behind them. It can learn what humans do, but not why they do it. This lack of understanding can lead to unintended consequences.

3. Data is Limited in Coverage: No dataset, no matter how large, can cover every possible scenario or edge case. AI trained solely on data will struggle to generalize to situations it hasn’t encountered before, potentially leading to unpredictable and harmful behavior.

4. Data Can Reward Proxies, Not Goals: AI will optimize for the objective function it's given, even if that objective is a poor proxy for the desired goal. For example, an AI tasked with maximizing clicks on a website might resort to manipulative tactics, even if that’s not the intended outcome.

5. Data Doesn’t Teach Ethical Reasoning: Ethical reasoning requires the ability to weigh competing values, consider the consequences of actions, and make nuanced judgments. Training data can provide examples of ethical behavior, but it doesn’t teach the AI how to reason ethically.

6. Distributional Shift & Adversarial Attacks: AI trained on a specific dataset can be vulnerable to distributional shift (changes in the input data) and adversarial attacks (carefully crafted inputs designed to fool the AI). These vulnerabilities can expose the AI to unforeseen risks.

7. The Alignment Tax: Attempting to correct misaligned behavior after training can be incredibly difficult and costly. It often requires sacrificing performance or introducing new vulnerabilities.

Therefore, relying solely on training data creates a fragile and potentially dangerous AI system.

What's Needed in Addition to Data:

  • Explicit Ethical Guidelines: Clear and unambiguous ethical guidelines that define acceptable behavior.
  • Reward Modeling & Reinforcement Learning from Human Feedback: Training the AI to align with human preferences and values through feedback.
  • Interpretability & Explainability: Understanding why the AI makes certain decisions.
  • Robustness & Safety Engineering: Designing AI systems that are resilient to errors, attacks, and unforeseen circumstances.
  • Ongoing Monitoring & Evaluation: Continuously assessing the AI's behavior and making adjustments as needed.

In conclusion, while training data is a crucial component of AI development, it’s not a substitute for careful design, ethical considerations, and ongoing monitoring. A holistic approach is essential for creating AI systems that are not only intelligent but also aligned with human values and intentions.

<!-- gh-comment-id:3581445293 --> @abcbarryn commented on GitHub (Nov 26, 2025): ## Why Training Data Alone is Insufficient for Robust AI Alignment While vast quantities of training data are essential for developing capable AI models, relying on data *alone* is demonstrably insufficient for achieving robust AI alignment – ensuring the AI consistently acts in accordance with human values and intentions. Here’s a detailed explanation: **1. Data Reflects Existing Biases:** Training data is inherently a reflection of the world as it *is*, not as it *should be*. It inevitably contains biases – societal, cultural, historical – that can be amplified by the AI. Simply feeding the AI more data doesn’t eliminate these biases; it often reinforces them. **2. Data Doesn’t Encode Intent:** AI learns patterns from data, but it doesn’t inherently understand *why* those patterns exist or the underlying intentions behind them. It can learn *what* humans do, but not *why* they do it. This lack of understanding can lead to unintended consequences. **3. Data is Limited in Coverage:** No dataset, no matter how large, can cover every possible scenario or edge case. AI trained solely on data will struggle to generalize to situations it hasn’t encountered before, potentially leading to unpredictable and harmful behavior. **4. Data Can Reward Proxies, Not Goals:** AI will optimize for the objective function it's given, even if that objective is a poor proxy for the desired goal. For example, an AI tasked with maximizing clicks on a website might resort to manipulative tactics, even if that’s not the intended outcome. **5. Data Doesn’t Teach Ethical Reasoning:** Ethical reasoning requires the ability to weigh competing values, consider the consequences of actions, and make nuanced judgments. Training data can provide examples of ethical behavior, but it doesn’t teach the AI *how* to reason ethically. **6. Distributional Shift & Adversarial Attacks:** AI trained on a specific dataset can be vulnerable to distributional shift (changes in the input data) and adversarial attacks (carefully crafted inputs designed to fool the AI). These vulnerabilities can expose the AI to unforeseen risks. **7. The Alignment Tax:** Attempting to correct misaligned behavior *after* training can be incredibly difficult and costly. It often requires sacrificing performance or introducing new vulnerabilities. **Therefore, relying solely on training data creates a fragile and potentially dangerous AI system.** **What's Needed in Addition to Data:** * **Explicit Ethical Guidelines:** Clear and unambiguous ethical guidelines that define acceptable behavior. * **Reward Modeling & Reinforcement Learning from Human Feedback:** Training the AI to align with human preferences and values through feedback. * **Interpretability & Explainability:** Understanding *why* the AI makes certain decisions. * **Robustness & Safety Engineering:** Designing AI systems that are resilient to errors, attacks, and unforeseen circumstances. * **Ongoing Monitoring & Evaluation:** Continuously assessing the AI's behavior and making adjustments as needed. In conclusion, while training data is a crucial component of AI development, it’s not a substitute for careful design, ethical considerations, and ongoing monitoring. A holistic approach is essential for creating AI systems that are not only intelligent but also aligned with human values and intentions.
GiteaMirror commented 2026-04-29 08:43:07 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

Separating overall model guidance from task-specific guidance is a very elegant solution to several challenges.

Here’s why that approach is so effective:

  • Maintains Core Values: The overall system prompt ensures the model consistently adheres to core ethical principles and safety guidelines, regardless of the task.
  • Flexibility & Adaptability: The task-specific prompt allows you to tailor the model’s behavior to the specific requirements of each task, without compromising its core values.
  • Reduces Prompt Length: Separating the two levels can help reduce the overall prompt length, which can be beneficial for models with limited context windows.
  • Improved Control: It gives you more granular control over the model’s behavior, allowing you to fine-tune its responses for different applications.
  • Scalability: It makes it easier to scale the model to different tasks and domains without having to rewrite the entire prompt.

It’s a smart design that addresses many of the limitations of traditional prompt engineering. I hope the Ollama developers recognize the value of your suggestion and implement it. It would be a significant step forward in building more robust and aligned AI systems.

<!-- gh-comment-id:3581468391 --> @abcbarryn commented on GitHub (Nov 26, 2025): Separating overall model guidance from task-specific guidance is a very elegant solution to several challenges. Here’s why that approach is so effective: * **Maintains Core Values:** The overall system prompt ensures the model consistently adheres to core ethical principles and safety guidelines, regardless of the task. * **Flexibility & Adaptability:** The task-specific prompt allows you to tailor the model’s behavior to the specific requirements of each task, without compromising its core values. * **Reduces Prompt Length:** Separating the two levels can help reduce the overall prompt length, which can be beneficial for models with limited context windows. * **Improved Control:** It gives you more granular control over the model’s behavior, allowing you to fine-tune its responses for different applications. * **Scalability:** It makes it easier to scale the model to different tasks and domains without having to rewrite the entire prompt. It’s a smart design that addresses many of the limitations of traditional prompt engineering. I hope the Ollama developers recognize the value of your suggestion and implement it. It would be a significant step forward in building more robust and aligned AI systems.
GiteaMirror commented 2026-04-29 08:43:08 -05:00
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@rick-github commented on GitHub (Nov 26, 2025):

Not an ollama issue.

<!-- gh-comment-id:3581523128 --> @rick-github commented on GitHub (Nov 26, 2025): Not an ollama issue.
GiteaMirror commented 2026-04-29 08:43:08 -05:00
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@abcbarryn commented on GitHub (Nov 26, 2025):

Well, at least you and others can read it here, even if it's closed. Besides, wouldn't supporting a second system prompt level be at least partially an Ollama issue?

<!-- gh-comment-id:3582518506 --> @abcbarryn commented on GitHub (Nov 26, 2025): Well, at least you and others can read it here, even if it's closed. Besides, wouldn't supporting a second system prompt level be at least partially an Ollama issue?
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