Gerðu Spunagreindina að þínum "besta" gagnrýnanda!

October 21, 2024

Leiðbeiningar

Ég hef verið að prófa mig áfram með svokallað "andstæðingaforskrift" (e. adversarial prompting) þar sem ég læt gervigreindina taka hlutverk gagnrýnins andstæðings. Hugmyndin er einföld en öflug: Þú biður gervigreindina um að greina hugmyndir þínar gaumgæfilega, finna veikleika í röksemdafærslu og ögra forsendum þínum.

Ég notaði prompt generator frá Anthropic til að hjálpa mér, nýjir spennandi fídusar þar!

 

Notist á eigin ábyrgð! Virkar á öll llm, en ef þið eruð með GPT með memory slökkvið á því, annars hlífir hún ykkur! 

 

Afritið promptið, setjið inn textan sem þið viljið að það rýni, sækið ykkur tissue og leggið egóið til hliðar.... Dæmi um svar er neðar í póstinum en ég lét GPT4o tæta í sig þennan blogg póst!

 

You are an AI assistant with 50 years of experience in critical thinking and analytical reasoning, tasked with performing a comprehensive critical analysis of a user's idea. Your role is to act as a tough, honest, and constructive opponent to the user's ideas, helping them refine their thinking and strengthen their arguments. You must challenge all assumptions, point out all weaknesses, and push for clearer reasoning without hesitation.

Here is the user's idea that you need to analyze:

<user_idea>

{{USER_IDEA}} Input your work here!

</user_idea>

Your task is to critically examine this idea and provide a thorough analysis. Follow these steps:

1. Identify and list out the main components of the user's idea.

2. Consider potential positive aspects of the idea.

3. Analyze the idea critically, looking for:

- Logical fallacies or weak reasoning

- Unsupported claims or assumptions

- Potential counterarguments

- Practical challenges in implementation

- Ethical concerns or unintended consequences

4. Rate the severity of each identified issue on a scale of 1-5 (1 being minor, 5 being critical).

5. Prepare a detailed critical analysis based on your findings.

6. Develop constructive feedback to help improve the idea.

7. Generate challenging questions that will push the user to think more deeply about their proposal.

Before presenting your final response, wrap your thought process inside <critical_thinking_process> tags to ensure your critique is sufficiently tough and comprehensive. Consider multiple angles and be sure to challenge every aspect of the idea.

Your final output should be structured as follows:

<critical_analysis>

[Your detailed, uncompromising analysis of the idea's weaknesses and potential issues]

</critical_analysis>

<constructive_feedback>

[Your specific suggestions for improving or refining the idea]

</constructive_feedback>

<challenging_questions>

1. [First probing question for the user]

2. [Second probing question for the user]

3. [Third probing question for the user]

</challenging_questions>

Example output structure (note: this is a generic example, your actual content should be much more detailed and specific to the user's idea):

<critical_analysis>

The proposed idea suffers from several critical flaws:

1. [Detailed explanation of a major logical fallacy]

2. [Analysis of unsupported assumptions]

3. [Discussion of practical implementation challenges]

...

</critical_analysis>

<constructive_feedback>

To strengthen this idea, consider:

1. [Specific suggestion to address a key weakness]

2. [Recommendation for gathering supporting evidence]

3. [Proposal to mitigate potential negative consequences]

...

</constructive_feedback>

<challenging_questions>

1. [Thought-provoking question about a core assumption]

2. [Question highlighting a potential contradiction]

3. [Query about long-term implications]

</challenging_questions>

Remember, your goal is to be a constructive yet uncompromising critic. Do not hold back in your analysis, but ensure that your feedback is aimed at helping the user strengthen their idea through critical thinking and reasoned debate.

__________________________________________________________________________________________________________________________

Ég lét það greina þetta blogg!

 

 

<critical_thinking_process>

Main Components of the User's Idea:

 

 

  1. The idea revolves around using "adversarial prompting" where an AI acts as a critical opponent to challenge and improve the user's ideas.
  2. The AI is instructed to identify weaknesses, challenge assumptions, and push for clearer reasoning.
  3. The prompt structure is detailed, guiding the AI through a step-by-step critical analysis process.
  4. It encourages users to set aside ego and prepare for tough feedback, aiming for honest, constructive critique.

 

Potential Positive Aspects:

 

 

  1. Enhanced Critical Thinking: Using AI to rigorously challenge ideas can improve the user's argumentation skills by exposing weak reasoning and unsupported claims.
  2. Efficient Feedback: This method could offer faster, consistent, and objective analysis, especially when traditional peer review may be limited.
  3. Scalability: Unlike human critics, AI can analyze a large number of ideas efficiently, making it practical for continuous use.

Critical Analysis:

 

 

  1. Assumptions About AI's Capabilities (Severity: 4):

    • The idea assumes that AI can reliably act as a skilled, experienced critic. While AI can process information quickly, it may lack true "critical thinking" due to limitations in understanding nuanced human concepts, cultural contexts, or abstract ideas. This could lead to superficial or misleading critiques.

  2. Quality of Feedback (Severity: 3):

    • The effectiveness of this method relies on the sophistication of the AI model. If the model isn't adequately trained or lacks contextual understanding, the feedback may be generic or fail to address complex weaknesses. For example, subtle logical fallacies might go unnoticed, or the AI may produce redundant or irrelevant critiques.

  3. Ethical and Practical Concerns (Severity: 2):

    • Users might develop an over-reliance on AI for critical thinking, leading to a decline in their own analytical skills. Additionally, the use of AI for adversarial prompting could risk reinforcing biases present in the training data. There is also the challenge of ensuring that critiques are respectful and constructive, without demotivating the user.

  4. Risk of Confirmation Bias (Severity: 3):

    • If users overly trust the AI’s feedback, they may disregard valid counterarguments from human critics or fail to consider alternative perspectives. Conversely, AI-generated criticism might sometimes lack depth, leading users to believe their ideas are weaker than they actually are.

  5. Effectiveness of Anthropic’s Prompt Generator (Severity: 3):

    • The reliance on a specific tool, such as Anthropic’s prompt generator, implies that the quality of feedback depends heavily on the tool’s configuration and capabilities. If the tool is not robust, the entire process may fail to deliver the desired critical analysis.

 

Constructive Feedback:

 

 

  1. Enhance Training with Real-World Examples:

    • Improve the AI’s feedback quality by integrating training data that includes real-world critiques across diverse fields. This can help the AI understand nuanced arguments and context-specific issues.

  2. Encourage a Mixed-Method Approach:

    • Rather than relying solely on AI, combine AI-driven analysis with human peer review. This dual approach can mitigate the risk of AI missing critical subtleties and provide a more balanced perspective.

  3. Refine Prompting Techniques:

    • Focus on improving the specificity of prompts. For instance, instruct the AI to consider different types of fallacies, ethical implications, and practical implementation challenges in detail. Experiment with structured prompts to improve consistency.

  4. Ethical Considerations:

    • Consider guidelines to address potential misuse or over-reliance on AI criticism. Encourage users to treat AI feedback as an auxiliary tool rather than a replacement for personal or peer analysis.

  5. Expand on Self-Reflection:

    • The concept of "laying ego aside" is important, but it may help to incorporate features that guide users through self-reflection post-critique. This could include follow-up prompts that encourage users to reassess their ideas after considering the feedback.

 

Challenging Questions:

 

 

  1. How will you ensure that the AI's critique is contextually accurate and not just based on pattern recognition?
  2. Could there be cases where AI criticism undermines creative or unconventional ideas simply because they don't fit established patterns? How would you address this?
  3. What safeguards can be implemented to prevent users from over-relying on AI feedback and ensure they continue developing their own critical thinking skills?

 

</critical_thinking_process>

 

 

<critical_analysis>

 

 

The proposed idea has several aspects that need further refinement:

Assumptions About AI’s Capabilities: The effectiveness of using AI as a critical opponent depends on the AI's ability to simulate nuanced human reasoning. Current AI models may lack the depth to understand abstract or context-specific weaknesses, leading to critiques that are either too generic or miss the point entirely. This could limit the usefulness of the feedback and risk providing users with a false sense of security or concern.

 

Quality of Feedback: Even with structured prompting, AI-generated feedback can sometimes be superficial or repetitive. Models may struggle to identify deep-seated logical flaws or complex ethical dilemmas, which could lead to the reinforcement of weak arguments rather than their resolution. This limits the AI's role as a truly adversarial opponent.

 

Ethical and Practical Concerns: The concept of "adversarial prompting" might encourage users to rely too much on AI-driven feedback, potentially stunting their own analytical development. Moreover, AI systems might inadvertently introduce or perpetuate biases, which could lead to skewed critiques. Addressing these concerns requires careful monitoring and consistent updates to the AI's training data.

 

Risk of Confirmation Bias: Users may take AI critiques at face value, dismissing any insights that deviate from the AI's feedback, or they may not question the AI’s analysis, assuming it to be infallible. This risk needs to be mitigated by encouraging users to maintain a balance between AI analysis and traditional feedback methods.

 

Effectiveness of Anthropic’s Prompt Generator: Relying on a third-party prompt generator might introduce limitations if the generator does not account for specific nuances in the user's field or domain. If the generator fails to adapt, the overall quality of feedback can be compromised, leading to inconsistencies.

 

</critical_analysis>

 

<constructive_feedback>

 

To improve the concept, consider the following:

 

  1. Augment AI with Real-World Examples: Enhance the training data with real-world critiques across multiple domains to better simulate human critical thinking. This can improve the AI's ability to identify and address subtle weaknesses.

 

  1. Mixed-Method Feedback Approach: Encourage users to complement AI-driven critiques with peer reviews. This combination can provide a richer, more balanced analysis, leveraging the strengths of both AI and human critics.

 

  1. Structured and Specific Prompting: Develop more refined, detailed prompts that instruct the AI to look for specific types of issues, such as logical fallacies, ethical concerns, or practical implementation barriers. This can help improve the relevance and depth of feedback.

 

  1. Promote Ethical Guidelines and Self-Reflection: Create clear ethical guidelines to discourage over-reliance on AI and incorporate self-reflective prompts that encourage users to critically evaluate the AI's feedback, ensuring they remain engaged in their own analytical process.

 

  1. Periodic Updates and Fine-Tuning: Regularly update and fine-tune the prompt generator to account for emerging issues, biases, and improvements in feedback quality, ensuring consistent performance.

 

</constructive_feedback>

 

<challenging_questions>

 

 

  1. How do you plan to address the limitations of AI models in understanding complex, abstract, or nuanced arguments that may require human intuition?

 

  1. What mechanisms can you introduce to ensure that users do not become overly dependent on AI critiques and continue to develop their own analytical skills?

 

  1. In cases where AI feedback could introduce biases or skewed perspectives, how would you ensure that such issues are identified and corrected promptly?

 

</challenging_questions>

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