Prompt engineering gets all the headlines. Syntax tweaks. Model updates. Token limits. But behind the scenes, the most consequential thing we’re engineering isn’t the prompt — it’s the relationships the prompt shapes.
When we introduce GenAI into team workflows, we’re not just playing with clever autocomplete. We’re negotiating meaning. Navigating ambiguity. And, sometimes, masking tension we don’t want to face head-on. Because the truth is: a prompt is never just a string of words. It’s a mirror. And occasionally, a smoke machine.
The Illusion of Clarity
One of the great seductions of AI is the sheen of clarity. You enter a beautifully structured prompt, and out comes something coherent, confident, and… subtly wrong. Not technically wrong. Just relationally misaligned.
Why does this happen? Because while the model can mimic structure, it can’t read the room. It doesn’t know that when Marketing says “insight,” they mean audience sentiment, but when Product says it, they mean funnel friction. It doesn’t see that when Engineering asks for “requirements,” they’re secretly begging for fewer pivots.
When those internal misalignments go unresolved, a well-written prompt only reinforces the illusion that everyone’s on the same page. We’re not. We’re just good at pretending.
Prompt Laundering and False Consensus
There’s a growing pattern in product teams: rather than face the discomfort of disagreement, we funnel the tension into AI. Let the model decide. Let it generate the list, pick the headline, suggest the next step.
But there’s a cost to this. It creates the impression of consensus without the pain of negotiation. The model becomes a proxy for group alignment — a neat, polite answer to a messy human question.
This is what we call “prompt laundering.” The model outputs something clean, so we act like the input process was, too. But often, it wasn’t. It was the result of vague direction, uneven understanding, or unspoken assumptions. The output hides the input’s dysfunction.
And worse: the more confident the model sounds, the less likely people are to challenge it. We start treating AI outputs like source-of-truth artefacts rather than springboards for critical thinking.
Prompt Design as Social Contract
So what’s the fix? We need to stop treating prompts as technical artefacts and start treating them as social contracts.
A good prompt doesn’t just instruct the model. It reveals the intent of the person behind it. It signals priorities, assumptions, values. And when shared across a team, it becomes a tool for alignment — or misalignment, if we’re not careful.
That means prompt design should be collaborative. Auditable. Feedbackable. A tool to expose misunderstanding early, not cement it late.
Before sharing that prompt with the team, ask: who might read this differently? Who isn’t in the room, but whose perspective matters? And most importantly: is this prompt designed to generate clarity — or just to avoid conflict?
Conclusion: Align the Humans First
The promise of GenAI in product teams isn’t speed. It’s surfacing assumptions. It’s showing us what we thought we meant — and giving us a chance to course-correct.
But that only works if we’re honest about the relational risks. Because before you fine-tune the model, you need to fine-tune the conversation. And that’s not a prompt you can automate.
It’s a habit you have to build.