Making AI chat capabilities understandable
How paired examples and separate capability controls explain what memory, tools and other chat features change before a visitor tries the assistant.
The problem
A polished chat answer can hide which capability made it possible. Visitors need to see the difference between a model answering from its context and a system using tools or earlier conversation turns.
The approach
Each scenario pairs an example without a capability with one using it. The contrasting labels, numbered examples and explanations make the mechanism easier to inspect.
A pixel journey connects the scenarios and their details. The same reader pattern, keyboard navigation and mobile split behavior used elsewhere keep exploration familiar.
The live assistant exposes capability choices separately. Tool use and conversation memory are explicit options; example conversations remain distinct from a newly generated answer.
What the implementation enables
A visitor can inspect a capability, compare several examples and choose what to enable when opening the assistant. The demonstration supplies context for judging the resulting answer.
How it is checked
Tests cover scenario selection, capability controls and chat rendering. Server-side tool schemas define the available inputs, and authentication protects live chat calls.
Limits and tradeoffs
Curated examples illustrate behavior; they do not establish reliability across all models or inputs. A tool result can still be wrong, and enabling memory does not guarantee that an answer is accurate.