Terms we use
Some of the systems shaping discovery, trust and decision-making are already familiar. The language around them often is not.
These are the terms we use at FabUX to describe how people, brands, services and answer engines increasingly interpret each other.
These terms sit closest to how people and systems interpret an organisation before a decision is made.
Systems that summarise, interpret and recommend information using AI. Examples include ChatGPT, Google AI Overviews, Perplexity and Copilot.
They increasingly shape what people see, trust and understand before they visit a website directly.
Designing for Decisions.
The FabUX approach to improving how people understand, trust and choose across digital, physical and AI-shaped experiences.
Studying what people actually do rather than relying only on what they say they do.
This may include eye tracking, customer research, field observation and journey testing. The aim is to understand behaviour in context.
How information, environments and experiences influence the decisions people make.
It looks at where confidence is built, where uncertainty appears and where people need clearer signals.
Clarity and ambiguity
Some problems look technical from the outside. Often, they begin with unclear signals, missing context or language that asks people and systems to do too much guessing.
The spaces where people or AI systems have to guess what something means.
Where ambiguity exists, assumptions fill the gap. FabUX helps reduce those gaps through clearer positioning, content and experience design.
When an AI system gives an answer that sounds confident but is wrong, incomplete or misleading.
Hallucinations often emerge from knowledge gaps, ambiguity, fragmented information, inconsistent messaging and weak digital signals.
AI systems attempt to complete missing context probabilistically. If an organisation has not clearly explained what it is and what it is not, there is more room for the system to guess.
This makes hallucination as much an organisational clarity problem as an AI problem. Clearer signals and stronger content ecosystems reduce ambiguity.
The cues that help people and systems understand what an organisation is, does, proves and stands for.
They include website content, naming, reviews, structured information, third-party references, imagery and the consistency of language across channels.
The connected body of content that helps people and answer engines understand an organisation.
Strong content ecosystems repeat important truths without becoming repetitive. They reduce confusion, support trust and make decisions easier.
Experience and trust
These are practical terms for looking at how people move through experiences, build confidence and decide what to do next.
AI systems that can take actions, use tools or make choices with limited human input.
As these systems become more common, organisational clarity matters because unclear information can lead to poor recommendations or misplaced confidence.
The full setting in which a person makes a decision. It includes the page, product, journey, brand signals, language, previous experience and emotional context.
FabUX treats decisions as lived experiences, not isolated clicks.
The details that help people feel informed, safe and confident enough to continue.
They can be explicit, such as reviews or guarantees, or quieter, such as consistent language, clear imagery and sensible next steps.
Helping people understand faster should also apply to the words we use.