Lately, I've been spending a lot of time thinking about how we can work with AI. Not just as a tool that churns out text, but as something more – perhaps a partner, or at least a very capable assistant in our co-creative work. This isn't a journey I'm on just by sparring with AI; it's deeply inspired by my collaborations with people like Floor de Ruiter and Lars Doyer, true champions of bottom-up approaches, and by foundational works like Floor's book on the subject. They've really instilled in me the profound value of ensuring people recognize their contributions in the end result – that feeling of "yes, this is ours."

My explorations with AI, often through deep dialogues with models like Gemini 2.5 Pro, have increasingly focused on a crucial question: how might these new technologies help us amplify this very human process of fostering connection and shared ownership? In a world grappling with AI's potential downsides, like misinformation, I'm keen to uncover its upside – its capacity to help accelerate our progress towards a more just society, where social connection and sustainable well-being are central. These dialogues often lead me to consider the essential principles that should guide us when we invite AI into our collaborative spaces.

A particularly exciting avenue I've been exploring is how we can guide AI to "listen" on a deeper level. While the inner workings of large AI models remain incredibly complex, I'm discovering that we can design our prompts – the way we communicate our requests – to help AI surface the underlying values present in human conversations. The idea isn't about AI magically understanding human intent, but about us explicitly asking it to identify these values from our input, reflect them back to us, and then demonstrate how these values can be woven into the fabric of any co-created output. This makes the link between what's deeply important to people and the concrete results we achieve together visible and tangible, which I believe is fundamental to fostering genuine connection and ownership. Transparency in this process then becomes a natural supporting element.

Before I go further, maybe it's good to briefly touch on what I mean by "prompt design." When we interact with these large AI models, we give them a prompt – a question, a request, a piece of text to work with. "Prompt design" is simply the art and craft of figuring out how to ask or tell the AI what you're looking for in a way that it's most likely to give you something useful, relevant, and aligned with your intentions. It's a bit like having a conversation; the better you frame your questions and share your context, the better the dialogue usually is. It's an iterative process, a bit of a dance, and you learn as you go.

So, how might we design prompts that support connection and ownership, with transparency as a key ingredient? Let me share two anonymized examples of prompts I've been developing and the thinking behind them, often for use with our AI partner in live sessions, Dembrane . I'll share the essence here, and for those interested, I'll include the more detailed prompts in a toggle you can open.

Designing these prompts is an iterative process, a co-creation with AI itself. My dialogues with AI models about societal trends and the nature of AI have profoundly influenced my approach.

The challenge of detail and traceability: One of the biggest challenges is getting at the nuances: distinguishing between unanimous agreement, points of tension, and areas needing further thought, and then having the AI's output reflect this in a way that remains traceable for participants. How might we go from raw transcripts to a comprehensive plan that still feels connected to the original voices?

My current thinking is that the solution lies in a step-by-step construction of the output. Instead of a direct leap from input to final plan, we can prompt the AI to first try and analyze underlying perspectives, core values, and interests present in the dialogue. Then, using that "lens" of shared understanding, the AI might build the subsequent output. This way, everyone involved can hopefully see their crucial concerns reflected, fostering a sense of being seen and heard. This is an ongoing exploration, especially as AI models become more capable with larger context windows.

Connection and ownership as leading values: While transparency is a vital supporting value, for me, the ultimate aim is to foster connection – to the process, to each other, to the outcomes – and a deep sense of shared ownership. If people know how the AI is working with their input, what its requests were, and what its limitations are, they can engage with its output more critically, constructively, and with a greater sense of agency.

The iterative dance: The most important message I want to share about the process of prompt design is the value of iteration and collaboration – with other humans and with AI. It's about clearly articulating your intentions. Sometimes, the best way to start is by asking the AI itself: "I want to achieve X. What questions might you have for me to help refine this goal and the prompt to get there?" And, crucially, it's about actually reading and understanding the prompts you (co-)create.

A powerful way to deepen this iterative dance involves a cycle of design, testing, and refinement:

This cycle of design, practical application, feedback, and refinement – using real-world results as a benchmark – can lead to surprisingly insightful adjustments and much more effective prompts. Remember, these AI models are doing their best to deliver on our requests, and sometimes they hit their limits; understanding that, and working with them iteratively through practical application, is part of the dance.

If you're a facilitator or change-driver looking to explore this, my advice is to start small and be curious. This isn't just about using AI in live transcription tools like Dembrane; it's about a broader mindset.

Think about your own preparation. Perhaps you're designing a Miro workshop, outlining a new exercise, or simply trying to sharpen the core goal of an upcoming session. This is where AI can be a powerful sparring partner.

A simple first step? Engage in a dialogue with an advanced AI model (again, I often use Gemini 2.5 Pro for these kinds of explorations). Share your thoughts: "Here's the workshop I'm planning, here's the exercise I'm considering, here's the goal I'm aiming for." Then, articulate your intentions for that session. And finally, ask the AI: "What questions might you have for me to sharpen these goals and get closer to the desired outcome? How might you help me make the process to get there more concrete, or the exercise more impactful?"

Actively seek out that partnership. The aim is to reflect on the intended result of your collaboration with AI, whether it's for designing a better workshop, a clearer process, or a more engaging exercise. Do you want participants to feel a deep sense of recognition? Do you want to maximize shared ownership and foster that bottom-up spirit? How might you then build those intentions into your prompts for your own design process, or even into the AI-assisted elements you might introduce in your sessions?

This journey of designing AI prompts for connection, ownership, and transparency is an ongoing exploration. By sharing our processes, our prompts, and our learnings, we can collectively help shape an AI-assisted future that is more human-centered, more co-creative, and ultimately, more aligned with the positive change we seek to facilitate.