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 offer a strong starting points"

"The smart defaults are either too broad or users must articulate their full intent upfront and then fall back on iterative chat-based back-and-forth to refine context. This creates unnecessary friction. Consider common use cases where a financial analyst who uploads a quarterly report, still has to spell out what should be extracted. Or a shopper searching for running shoes on Perplexity still faces broad, unfocused results and must manually narrow them down based upon his preferences."

"If context is so important, why do we make users work so hard to provide it? 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."

Context management provides AI models with signals that shape behavior and outputs, yet current approaches treat context crudely and demand explicit user specification. Smart defaults, context filters, and style controls are helpful but often too broad or require users to state full intent and engage in iterative refinement, creating user friction. Common cases include analysts who still must specify extraction details and shoppers who must manually narrow broad search results. Predictive UX substitutes anticipation for explicit instruction by inferring intent from typing behavior and patterns. Inline intent refinement surfaces suggested prompt improvements during composition, reducing time-to-value across search, information extraction, and content creation.

Read at Medium