When people use generative AI at work, a familiar pattern repeats like a sitcom rerun. Someone faces a real decision—what model to ship, which architecture to deploy, which policy to roll out. They open an LLM, type a single prompt, skim the fluent answer, tweak it slightly, and copy the most convincing-looking output into a document. Six months later, when things fail, there’s no trace of what alternatives were considered, how uncertain the team really was, or why one path was chosen over others. Just a polished paragraph that felt right—once. What’s missing isn’t more AI power. It’s the habit of explicit human reasoning. This article introduces a practical reasoning pattern I’ve developed and taught in my own work: Probabilistic Multi-Variant Reasoning, or PMR. It’s not a new mathematical theory or algorithm. It’s a disciplined, human-centered approach to decision-making with LLMs—treating the model not as an oracle, but as a scenario generator with weighted options. PMR is for anyone using AI to make high-stakes choices—designing systems, managing risk, or navigating uncertainty. GenAI makes this kind of reasoning cheap and fast. The pattern itself applies wherever decisions are made under ambiguity, especially when consequences matter. The core shift is simple: stop treating the LLM as an answer machine. Start treating it as a tool to surface multiple plausible futures, assign rough probabilities, and expose risks and trade-offs in plain language. Then, argue with the output. Adjust the weights. Only then decide. This isn’t about perfect math. It’s about structure. It borrows from Bayesian thinking—updating beliefs with evidence—but keeps it informal. It draws from decision theory: probability alone isn’t enough; you must weigh outcomes and consequences. And it embraces the ensemble idea: multiple imperfect views are better than one “perfect” answer. To illustrate, imagine choosing a fraud detection model. A single-shot prompt might return a glowing endorsement of a deep learning model. But PMR asks: “Propose three distinct approaches—simple, moderate, advanced. For each, estimate the chance of success, implementation effort, operational risk, and cost of failure.” You get three stories, not one. Now, the team debates: “Is that 85% success rate realistic in our environment?” “Can we afford the rollback complexity?” “Is the 70-point gain worth the 9 out of 10 risk score?” The numbers aren’t gospel—they’re conversation starters. This same pattern works in cloud architecture. Instead of blindly adopting a managed service because it sounds reliable, PMR surfaces trade-offs: one option may be cheap but fragile; another expensive but bulletproof. You weigh likelihood against impact, not just likelihood alone. PMR isn’t limited to AI. It’s a general habit. Use it when choosing coding patterns, crafting messages for different audiences, or preparing for leadership reviews. The LLM helps generate options and surface costs, but the human does the thinking. Of course, PMR has pitfalls. There’s fake precision—treating model-generated probabilities as facts. There’s confirmation bias—using PMR to justify a pre-existing preference. There’s the risk of generating only variants of the same flawed idea, or missing the one scenario that actually matters. And there’s context bleed—letting the model import ideas from past conversations. The solution? Question the questions. Ask, “What’s missing?” Invite real experts. Challenge the model: “Convince me I’m wrong.” And always remember: the model proposes, humans dispose. PMR isn’t about replacing judgment. It’s about protecting it. It’s a way to stay grounded when AI sounds too confident. It’s a reminder that uncertainty has shape—and how we reason about it is still our responsibility. Try it next time you reach for the model. Ask for three options. Assign rough probabilities. Lay out pros and cons. Argue. Decide. You’ll think more clearly than most people who’ve outsourced their reasoning to a fluent AI response. And you’ll keep the human mind where it belongs: in charge of what counts as a good reason.