Not a prompt list. Not a feature tour. This is what changes when you know what you are trying to solve before you open the chat window.
Let me describe a scenario that will probably sound familiar.
You have a project due. The timeline is tighter than it should be. You open your AI tool, type something like “write me a scenario for a course on difficult conversations,” get something back that is roughly usable, clean it up, drop it into Rise, and keep moving.
That is not wrong. That is Tuesday.
But here is what I want to ask: when your stakeholder comes back and asks you why you designed it the way you did, or what you were hoping would be different after someone completed it, do you have an answer that reflects actual thinking? Or do you have a polished output with no story behind it?
This is the difference between reactive AI use and intentional AI use. And it matters a lot more than most of us realize right now.
The Question That Changes Everything
Intentional AI use does not start with a prompt. It starts with a question.
What am I trying to solve? Who is this for, and where are they right now relative to where they need to be? What should be different, for them or for the organization, after they engage in this learning? What would tell me this worked?
When you can answer those questions before you open the chat window, your AI use changes. Not because the tool works differently, but because your inputs are grounded in something real. The output reflects your thinking instead of replacing it.
Here is what that looks like in practice, using a real course I have been working with: a Rise eLearning course called How to Have a Difficult Conversation with Your Leader, designed for all employees at a health system, not just managers.
Step One: Name the Business Problem Before You Build
Before I touched a prompt for this course, I worked through the framing. The goal stated in the course was to help employees navigate difficult conversations with their leaders to achieve the best possible outcomes. That is a good learning objective. It is not yet a business case.
The business problem underneath it is more specific: employees who cannot have direct, productive conversations with their leaders stay stuck, disengage, or leave. Managers who are never challenged stay in blind spots that affect team performance. HR gets escalations that could have been resolved earlier. Psychological safety stays low. Retention suffers.
That is the story. Not “we built a course on difficult conversations.” We designed a learning experience to address a specific gap in how this workforce navigates upward communication, because that gap has real organizational consequences.
AI did not generate that framing. I brought that framing. AI helped me sharpen and articulate it.
Step Two: Prompt with Specificity, Not Generality
Here is the thing about prompting that most of us figured out through trial and error: vague prompts produce vague output. Always.
When I want AI to help me build the stakeholder communication around this course, I do not ask it to “write a summary of my training.” I tell it who I am, what I built, who it is for, what problem it was solving, and what I need. And then I ask it a specific question.
The difference in output quality between those two approaches is not subtle. A specific, contextualized prompt gives me something I can use. A general prompt gives me something I must rebuild from scratch.
Here is the actual prompt I would use to start building the stakeholder story for this course:
I am an instructional designer at a mid-size health system. We just finished a Rise eLearning course called How to Have a Difficult Conversation with Your Leader. It is designed for all employees, not just managers. The goal is to help employees navigate upward conversations more effectively. Before I prepare for a stakeholder debrief, help me identify three to four business problems this course was designed to address, written in language a VP of HR or Operations would recognize and care about.
What comes back should surface language I would not necessarily have led with in the course brief: retention, psychological safety, leadership trust, reduced HR escalations. That is not AI inventing a story. That is AI helping me find the business language that was already underneath the learning design decision.
Step Three: Use the Output as a Starting Point, Not a Final Draft
Here is what I see a lot: someone gets a solid AI output, pastes it directly into an email or a slide, and sends it. I understand the impulse. The deadline is real.
But the output is a draft, not a deliverable. Your job is to read it as a subject matter expert and decide what is accurate, what needs to be adjusted for your specific context, and what does not belong. That editorial judgment is yours. It is also, frankly, what makes the final product sound like you instead of like a language model.
For this course, I would take the business problem language AI surfaces, identify the two or three that match the organizational context I designed for, and build my stakeholder narrative from there. The rest gets set aside. Not because it is wrong, but because specificity is always more persuasive than comprehensiveness.
What This Looks Like When It Works
When you are using AI with intention, a few things start to shift in how you show up.
You walk into stakeholder meetings with a story, not just a status update. You can explain not just what you built but why you built it that way, what you were trying to change, and how you will know if it worked. You sound like someone who was solving a problem, not someone who was executing a request.
That is the shift. It is not about the tool. It is about what you bring to the tool and what you do with the output.
The VIP workshop on July 20th is where I demonstrate this full workflow live, from finished course to stakeholder-ready communication, using prompts you can take directly into your own practice. If you want to watch the process from start to finish, that is where to be.
Want to watch the full workflow live? Join #IgniteLearning — free 7-day VIP trial. https://zps.circle.so/checkout/ignite-learning
About Dani Watkins
Dani is the founder of #IgniteLearning and the owner of Zenith Performance Solutions. She’s an instructional designer, trainer, and eLearning developer who creates practical resources for in-house L&D professionals. She presents regularly for Training Magazine and believes deeply that good learning design changes outcomes — and that the right tools make that possible inside real organizational constraints.