How can AI UI capture intent?

"Context management is unarguably one of the most important aspect that provide AI models context to shape their behaviour and results. When users upload documents, ask questions, or provide instructions, they're essentially teaching AI models how to behave and what outcomes to deliver. Yet despite this fundamental importance, most AI products handle context in surprisingly crude ways. Current patterns like smart defaults in Claude homepage, context filters in GitHub Copilot or style controls in Adobe Firefly"

"Contrary, Predictive UX points to an alternate approach. Instead of waiting for users to articulate every step, systems can anticipate intent based on behavior or common patterns as the user types. Apple Reminders suggests likely tasks as you type. Grammarly predicts errors and offers corrections inline. Gmail's Smart Compose even predicts full phrases, reducing the friction of drafting entirely. The goal is to reduce time-to-value"

Effective context management supplies AI models with the situational signals needed to shape behavior and outputs. Current implementations often rely on coarse controls like smart defaults, context filters, and style toggles, forcing users to fully articulate intent or iterate through back-and-forth refinement. Common workflows for analysts or shoppers still require manual narrowing despite uploaded documents or initial inputs. Predictive UX proposes anticipating intent from typing behavior and common patterns, offering inline suggestions and prompt improvements. Examples include autocomplete for tasks, inline corrections, and phrase prediction. Inline intent refinement aims to reduce friction, speed time-to-value, and improve relevance across search, extraction, and content creation.

Read at Medium