The core failure mode of large language models is not hallucination. It is context starvation. A model will produce a confident, coherent, well-reasoned answer to the wrong version of your problem because you gave it the wrong frame to work inside. The UX Collective piece 'Context matters... A lot' makes this concrete: even major platforms like ChatGPT, Gemini, Claude, and Copilot are now visibly redesigning their core interfaces around context management, not just prompt input. That is not a feature update. That is a structural admission.
The article draws a sharp line between prompt engineering and context design. Prompting is about phrasing. Context design is about what history, constraints, and definitions of 'good' are loaded into the system before it reasons at all. The author cites Google Research on retrieval-augmented generation and a 2025 METR study on long-task execution to show that models do not flag when they are missing critical information. They fill gaps with plausible assumptions and keep moving. The cold plunge example in the piece is worth reading in full: a product requiring under 1 PSI gets generic water pressure advice because the specific unit was never shared.
The argument that follows is what makes this worth your time beyond the summary. The shift from one-off interactions to persistent, structured context is redefining how AI products are architected. The author frames this as a new design discipline, contextual intelligence management, sitting above both UX and prompt engineering. If you build AI products, or evaluate them, the patterns being standardized across major platforms right now are the clearest signal of where product design is heading next.
[READ ORIGINAL →]