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How can AI UI capture intent?. Exploring contextual prompt patterns… | by Sharang Sharma | UX Collective

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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 but they share a limitation. 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 result

UX Product Management Product Design Conversational AI Editor Picks How can AI UI capture intent? Exploring contextual prompt patterns that capture user intent as it is typed Sharang Sharma 5 min read · Sep 8, 2025 -- 14 Listen Share Press enter or click to view image in full size 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 im

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