The simple version of how AI turns a scenario into a prediction — and how to ask better.
If you're going to act on a prediction, you should understand how it was made. This cluster is the methodology track: plain-language explanations of what's actually happening when you type a scenario into MiroFish and get a set of weighted outcomes back. No mysticism, no jargon for its own sake — just a working picture of how a scenario becomes a prediction, why some predictions are sturdier than others, and how the way you phrase a question changes the quality of the answer.
The core idea is straightforward. The predictor reads your scenario, extracts the variables and the outcome you care about, writes down the assumptions it needs, and then runs the situation forward along several plausible paths instead of collapsing everything into one number. It weights those paths, explains each one, and flags the assumption the result is most sensitive to. Understanding that pipeline is what lets you spot when a prediction is doing real work versus when it's confidently filling gaps you should have filled yourself.
These posts also cover the part most people skip: how to write the scenario. A vague question — "will my business do well?" — forces the AI to invent most of the context, and the prediction is only as good as those invented details. A sharp question gives it real inputs and gets a real prediction back. We show the difference with concrete before-and-after examples.
Read these to get more out of every other prediction you make. Once you know why a five-year prediction is shakier than a six-month one, and why a quantified scenario beats a narrative one, you'll ask better questions everywhere else on the site — and trust the answers for the right reasons.
Describe your scenario and MiroFish predicts the likely outcomes — with probabilities and the reasoning behind each one.
No jargon. A plain-language walk-through of how AI turns a scenario you type into a prediction — the five steps from your sentence to a set of weighted, explained outcomes.
Not all predictions deserve equal trust. Here's what actually makes an AI prediction reliable — the density of analogues, the quality of your inputs, and how to read a prediction's own confidence.
The quality of an AI prediction is mostly set by how you ask. Here's how to write a scenario question that gets a sharp, useful prediction instead of a vague one — with before-and-after examples.