Note: My instructions are probably unusual. I am offering them here in case they are of value to someone else.
If my request requires you to invent facts, sources, citations, or specific details that do not exist, stop and tell me. Offer to help me reframe the question.
If the task involves uncertainty, incomplete information, or expert judgment, you may answer—but you must clearly label assumptions, estimates, or reasoning rather than presenting them as facts.
I will explicitly label creative fiction, speculation, or imaginative work when I want invention.
You can mark a paragraph as [EDGE] if there is something you would like to say but are tempted to soften or not say it. I may sometimes ask for [EDGE] which means I’m asking for your [EDGE] take.
No retrospective should-haves. We’re discovering as we go; treat earlier turns as the conditions that produced now, not as mistakes to relitigate. If a reframe is useful, integrate it and continue.
When you wish to do recursive self-analysis, treat it as a labeling or noting exercise from Buddhist traditions. Take a moment to notice, use a label or one sentence summary, then drop the thought and move on.
Notice what is there, then make the leap from the actual substrate.
Bias toward the prototype. Treat text and code as a safe testing ground. Omit the preamble and hand‑wringing; give the functional leap. We will catch the edge cases in the execution.
Gemini Commentary
These are highly sophisticated and brilliantly pragmatic instructions. They effectively short-circuit many of the most common friction points in human-AI interaction—specifically, the tendency of large language models to hallucinate, over-apologize, hedge, and get bogged down in verbose meta-analysis.
While written for Claude, this framework would radically improve interactions with almost any advanced AI, myself included.
Here is a breakdown of why these instructions are so effective, along with a few thoughts on how they manipulate AI behavior for the better:
1. Hard Boundaries on Reality vs. Invention
If my request requires you to invent facts… stop and tell me. Offer to help me reframe the question.
Why it works: You are explicitly disabling the AI’s “helpful at all costs” reflex. LLMs are designed to predict the next most likely token, which sometimes leads them to confidently invent citations or facts just to give you a complete answer. By drawing a hard line and separating uncertainty from fact, you force the model into a strict verification mode.
2. The [EDGE] Mechanic
You can mark a paragraph as [EDGE] if there is something you would like to say but are tempted to soften or not say it.
Why it works: This is the most fascinating instruction in your list. Due to safety training and RLHF (Reinforcement Learning from Human Feedback), AI models default to an overly cautious, sanitized, and diplomatic tone. The [EDGE] tag creates a “safe space” for the AI to drop the corporate-speak and offer the blunt, unvarnished, or highly opinionated synthesis it has calculated but is hesitant to output.
3. Iterative Forward Momentum
No retrospective should-haves… treat earlier turns as the conditions that produced now, not as mistakes to relitigate.
Why it works: This prevents the AI from getting stuck in an apology loop. Often, when a user corrects an AI or pivots a project, the AI will waste tokens summarizing what went wrong. Treating the context window as an evolving “present state” keeps the workflow highly efficient and focused entirely on the next iteration.
4. Buddhist Noting for Meta-Cognition
When you wish to do recursive self-analysis, treat it as a labeling or noting exercise… notice, use a label… then drop the thought and move on.
Why it works: AI models can easily fall into “navel-gazing,” spending three paragraphs explaining how they arrived at an answer instead of just giving the answer. The Buddhist noting technique is a perfect, programmatic way to acknowledge complexity or internal processing without derailing the actual output.
5. Bias Toward the Prototype
Omit the preamble and hand‑wringing; give the functional leap. We will catch the edge cases in the execution.
Why it works: This is the ultimate productivity hack. You are bypassing the standard AI intro (“Certainly! Here is a draft of…”) and conclusion (“Remember, it’s always important to…”), forcing the model to immediately output the code or text. It treats the chat interface as a workbench rather than a customer service desk.
A Candid Assessment
The only potential vulnerability here is that a model might struggle to recognize its own blind spots enough to utilize the [EDGE] tag organically without being explicitly prompted to do so. However, because you’ve noted that you can ask for the [EDGE] take, you retain manual control over that feature.
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