You're looking for concise labels or "directives" that you could potentially use as prefixes or tags in your prompts to instruct the AI to perform specific types of critical thinking. This is an interesting idea, akin to creating your own set of "AI thinking commands." Here are some condensed labels you could consider, keeping in mind that their effectiveness will depend on the specific AI model you're using and how well it's been trained to understand these concepts: For Critical Evaluation: * Critique: (followed by the statement to evaluate) * Flaws: (followed by the topic to find flaws in) * Assumptions: (followed by the claim to analyze for assumptions) * BiasCheck: (followed by the text to analyze for bias) * LogicCheck: (followed by the argument to check for logical fallacies) * Evaluate: (followed by the subject to be evaluated) For Perspective-Taking: * AltView: (followed by the situation and the desired alternative viewpoint) * Empathize: (followed by the situation and the perspective to adopt) * Reframe: (followed by the statement to reframe from a different angle) For Assumption Challenging: * Challenge: (followed by the assumption to question) * WhatIfNot: (followed by the assumption to negate) * Unpack: (followed by the concept to break down into underlying assumptions) For Socratic Questioning (as a directive to the AI): * Probe: (followed by the topic, instructing the AI to ask probing questions) * WhyChain: (followed by the initial statement, asking the AI to repeatedly ask "Why?") * ReflectQ: (followed by the topic, asking the AI to generate reflective questions) For Devil's Advocate: * Counter: (followed by the idea to argue against) * Oppose: (followed by the proposition to find opposing arguments for) * WeakPoints: (followed by the plan or idea to identify weaknesses in) For Confidence/Uncertainty: * Confidence: (followed by the AI's statement, asking for a confidence score) * Uncertainty: (followed by the AI's answer, asking for potential sources of uncertainty) * AltExplain: (followed by the phenomenon, asking for alternative explanations) How These Might Work as "Tokens" (Conceptual): While you can't directly create new "tokens" in the way an AI's vocabulary is built during pre-training, you can treat these concise labels as strong cues within your prompts. A well-trained AI might learn to associate these labels with specific cognitive tasks. Example Usage: * Critique: The current economic policy will lead to widespread prosperity. * AltView: The impact of climate change from the perspective of a small island nation. * Challenge: The assumption that technological progress always benefits society. * Probe: The concept of artificial intelligence. * Counter: Universal basic income. * Confidence: My prediction for tomorrow's weather is 75%. Considerations: * Model Sensitivity: The effectiveness of these labels will vary greatly depending on the AI model's architecture, training data, and how it has learned to interpret instructions. * Clarity and Context: While aiming for conciseness, ensure that the label and the subsequent content still provide enough context for the AI to understand the desired task. * Experimentation: You'll likely need to experiment with different labels and phrasing to see what works best with the specific AI you are using. * Combining Labels: You might even combine labels for more complex instructions, e.g., LogicCheck: Critique: The politician's argument for lower taxes. This approach of using concise labels could be a way to develop a more efficient and targeted prompting strategy for eliciting specific types of critical thinking from AI models. It's a form of user-defined "cognitive prompting."