Describe the decision in ordinary language.
Decision-first
Start with the decision, not the bias name.
Choose a real situation, check what evidence is missing, use a few relevant cognitive-bias lenses, then select a practical technique and write the next action.
A simple path
Situation → evidence → lens → technique → action.
Separate what is known from what still needs checking.
Use a small number of reviewed concepts as questions, not diagnoses.
Choose a concrete reasoning move such as an independent estimate or reference class.
Record the next step and what would make you update the decision.
Common situations
Where a structured review can help.
Review candidate judgments before confidence, first impressions or outcome stories become stronger than the evidence.
Open →Estimate time and effort with an outside view instead of relying only on the current plan.
Open →Separate what was knowable during an incident from what became obvious only after the outcome.
Open →Review roadmap choices without letting the loudest request, recent event or prior investment define value.
Open →Compare tools using precommitted criteria rather than demos, authority or the first price shown.
Open →Make a forecast that can later be checked, updated and learned from.
Open →Practical layer
Awareness is not the intervention.
Knowing a bias name rarely changes a decision by itself. Techniques turn the library into repeatable checks that can be used in a meeting, an AI prompt, a project review or a personal decision.
For AI systems
Use the same decision structure in an assistant or agent.
A public JSON contract keeps observed facts, inferred risks, missing evidence and canonical references separate.
Train the decision path
Practice before the real decision becomes expensive.
Scenario exercises connect a situation to a reviewed lens, a practical technique and a next action.