The Context Sandwich concept is appealing and genuinely useful, but I think it could be expanded to address a wider range of scenarios. My version is considerably more detailed while still addressing the core elements of who you are, what you need, and why. It additionally covers your constraints, what you already know (so the AI doesn't recommend things you've already tried), what success looks like, what failure looks like, and the format for the response. By including these extra dimensions such as defining good and bad outcomes, noting urgency, and especially listing approaches you've already attempted my version provides far more context than the standard three-part structure. This ensures the AI won't repeat suggestions you've already explored. I'll also share an example of how I use my version. but here is the template below 1. Here is who I am: [Your role, expertise, limitations, and perspective—e.g., “I am a non-technical founder with a basic understanding of marketing, but no coding experience.”] 2. Here is what I need: [Specific output, decision, or help—e.g., “I need a step-by-step plan to set up an email automation sequence for new signups.”] 3. Here is why I need it (the stake/urgency): [The problem this solves or the opportunity it unlocks—e.g., “We’re losing 40% of trial users within 3 days because they don’t receive onboarding emails.”] 4. Here are my constraints (time, budget, resources, skills): [e.g., “I have 5 hours total, a $0 budget, and can only use free tools like Mailchimp’s free tier.”] 5. Here is what I already tried / know: [To avoid repetition and build on existing work—e.g., “I set up a welcome email manually, but open rates are under 10%.”] 6. Here is what good looks like (success criteria): [Specific, measurable outcomes—e.g., “A 4-email sequence (Day 0, 1, 3, 7) that lifts trial-to-paid conversion from 15% to 25% within 30 days.”] 7. Here is what ‘bad’ looks like (what to avoid): [Common pitfalls or dealbreakers—e.g., “Don’t suggest paid tools, complex coding, or more than 5 emails total.”]

Grand Rising, Leaders. ✨💜🏛️ We’ve spent years asking: “What can this AI do?” The executive question is rapidly becoming: “What evidence do you have that it can be trusted to do it?” 📋🔐 Sierra’s conversational AI system has achieved AIUC-1 certification, following independent auditing and extensive testing designed specifically around the behavior of AI agents. And THAT caught my attention. 👀 Because these agents are no longer just generating paragraphs. They may authenticate users, access business systems, interpret company policies, handle sensitive information, and take actions inside real workflows. That changes the governance conversation completely. 🏛️ From an executive leadership perspective, capability alone is no longer enough. An organization should be able to demonstrate: 🔐 AUTHORIZATION — What is this agent actually permitted to do? 🧱 BOUNDARIES — What happens when someone attempts to push it beyond its assigned role? 📑 AUDITABILITY — Can leadership reconstruct what happened, what information was accessed, and why an action occurred? 🛑 STOPPABILITY — Can human authority intervene, revoke access, or terminate execution when necessary? Here’s the part I believe boards and business owners need to pay very close attention to: Certification should never become permission. A certificate may provide evidence that controls were tested. It does NOT replace organizational governance. It does NOT decide your agent’s authority. And it certainly does NOT eliminate executive accountability. 👑📋 The emerging Agentic Age may create an entirely new expectation: “Show me the controls before you show me the capabilities.” 🔥 BOARDROOM QUESTION: Would you allow an AI agent into a high-impact business workflow because the vendor was independently certified? OR Would certification only earn the agent permission to enter your organization’s own governance-testing process? I’m choosing the second conversation. 👑🛡️ Because trust should be tested, documented, limited, monitored—and revocable.