Domain owner
Checks whether actors and constraints match reality.
What If Scenario Generator AI
What If Scenario Generator AI for evidence-led scenario rehearsal
Use MiroFish when what-if branches that name assumptions and next tests depends on what-if prompt, branches, and signals reacting differently. Bring seed material for what-if prompt, run reaction rounds around branches, and review signals changes as decision support, not a guaranteed prediction.
Operating facts
Keep what-if prompt and branches limits visible before opening the console.
What-if prompt workflowSeed material and graph context let what-if prompt and branches react inside a bounded MiroFish world.
Branches boundaryTreat branches reactions as decision support, not as a guaranteed prediction.
Useful inputThe first useful run needs what-if prompt, branches, signals, timing, constraints, and the strongest contrary signal.
Scenario readiness check
Check whether the what-if prompt brief is ready for a MiroFish run around what-if branches that name assumptions and next tests.
Use this quick tool before opening the console for what-if prompt and branches. A strong first run names signals, the pressure point, a review owner, and one outside validation move.
Preparation detail
Give what-if prompt enough context to produce a useful disagreement.
For what-if branches that name assumptions and next tests, begin with the moment when what-if prompt can change the path. Add what branches already knows, what signals might ignore, and which constraint would make the decision reversible. A narrow what-if prompt brief helps the report produce disagreement you can inspect instead of a smooth branches story that feels confident but cannot guide the next action.
Use concrete material from what-if prompt, branches, signals, timing, constraints, and the strongest contrary signal and label the parts that are still weak. If the what-if prompt note is old, the branches move is speculative, the signals source is ambiguous, or the signal came from a small sample, say that directly. MiroFish can then keep strong evidence separate from convenient assumptions while it builds reaction rounds.
Before acting, compare the report with branches changing the interpretation of what-if branches that name assumptions and next tests and then check what-if prompt against signals before treating the branch as useful. That outside check for signals should be small enough to complete quickly: one customer call, one support search, one analytics pull, one expert read, or one revised prompt with a single changed condition. The point of the first run is to improve judgment, not to outsource it.
MiroFish uses seed material from the brief to keep what-if prompt, branches, and signals anchored to the same facts. For what-if branches that name assumptions and next tests, that means the report should show which claim each actor accepted, which claim each actor resisted, and which missing detail changed the branch. If the report cannot name those links, improve the input before spending attention on a larger run.
Notebook prompt: ask what-if prompt what would make what-if branches that name assumptions and next tests feel urgent, ask branches what proof would be dismissed, and ask signals which missing fact would reverse the branch. Then record the exact what-if prompt sentence in the report that changed your confidence. If no sentence changes confidence, the next move is not a bigger run; it is a better source packet, a narrower actor list, or a validation check outside the tool.
What-if prompt brief naming
Sharpen the what if scenario generator ai brief before the run starts.
The what if scenario generator ai run should stay tied to one decision boundary, one source packet, and one validation owner. Use what if scenario map, scenario generator AI review, and what if branch generator as working handles so what-if prompt, branches, and signals review the same scenario from different angles.
what if scenario map: baselineUse what if scenario map for the branch where the expected story still holds around what-if branches that name assumptions and next tests.
scenario generator AI review: resistanceUse scenario generator AI review when signals from branches change the interpretation or expose a weak assumption.
what if branch generator: validationUse what if branch generator for the outside check that tests whether signals would change the next move.
During review, keep what if scenario map in branch notes, scenario generator AI review in evidence notes, and what if branch generator in validation notes. The next rerun should use the same actors, the same evidence boundary, one changed condition, and a clearer reason to continue or stop.
Scenario angle
The useful question is where what-if branches that name assumptions and next tests breaks.
This page is worth its own route because the reader needs a bounded rehearsal around what-if prompt, branches, signals. Start by asking which role can change the story first, then keep that role visible through the report review.
Human review
Give reviewers a concrete job.
Evidence owner
Checks whether the source packet supports the strongest claims.
Decision owner
Decides which branch changes the plan.
Rerun plan
Change one assumption after the first read.
The second run should keep the same source packet and actors, then change exactly one condition: timing, evidence strength, branches priority, channel, or constraint.
Evidence choice
Pick source material that can be challenged later.
The first run should include what-if prompt, branches, signals, timing, constraints, and the strongest contrary signal. If a source is old, ambiguous, or politically loaded, mark it before opening the console so the report does not treat a weak claim as settled.
Hard facts
Keep the operating limits visible.
What-if prompt workflowSeed material and graph context let what-if prompt and branches react inside a bounded MiroFish world.
Branches boundaryTreat branches reactions as decision support, not as a guaranteed prediction.
Useful inputThe first useful run needs what-if prompt, branches, signals, timing, constraints, and the strongest contrary signal.
Pressure map
Watch who turns the scenario first.
The first strong reaction is rarely the whole outcome. Track whether branches changing the interpretation of what-if branches that name assumptions and next tests, which group repeats the frame, and which missing fact lets the pressure grow.
Outside check
Leave with one verification move, not a pile of guesses.
The best next step after the first report is to check what-if prompt against signals before treating the branch as useful. That keeps the simulation useful without pretending it measured the real world.
First run
Copy a bounded brief into MiroFish.
Run this scenario with the source packet, the decision boundary, the actor roles, known objections, and the signals that would change the conclusion. Return reaction branches, weak assumptions, and one validation plan. Do not treat the report as a guaranteed outcome.
Review owner
Name the person who can say the branch is weak.
Before using the output, assign one reviewer to challenge what-if prompt, one to challenge branches, and one to decide whether signals changes the next action.
FAQ
What If Scenario Generator AI FAQ
What should I prepare for what if scenario generator ai?
Prepare the decision boundary, source notes, actor roles, known objections, timing, and the signals that would change the result.
Can what if scenario generator ai replace real evidence?
No. Use it to generate hypotheses, pressure points, and validation questions, then confirm important claims with real data or accountable review.
What does MiroFish return?
A structured report with reaction paths, weak assumptions, evidence gaps, and follow-up questions you can challenge.
When should I rerun it?
Rerun after changing one important assumption, such as the actor list, timing window, evidence strength, or public message.
Next paths