Open-ended prompts overwhelm new users who don't know what's possible. A better approach is to productize AI into specific features. Use familiar UI like sliders and dropdowns to gather user intent, which then constructs a complex prompt behind the scenes, making powerful AI accessible without requiring prompt engineering skills.
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People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.
With models like Gemini 3, the key skill is shifting from crafting hyper-specific, constrained prompts to making ambitious, multi-faceted requests. Users trained on older models tend to pare down their asks, but the latest AIs are 'pent up with creative capability' and yield better results from bigger challenges.
Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.
The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.
The best UI for an AI tool is a direct function of the underlying model's power. A more capable model unlocks more autonomous 'form factors.' For example, the sudden rise of CLI agents was only possible once models like Claude 3 became capable enough to reliably handle multi-step tasks.
A major hurdle in AI adoption is not the technology's capability but the user's inability to prompt effectively. When presented with a natural language interface, many users don't know how to ask for what they want, leading to poor results and abandonment, highlighting the need for prompt guidance.
The best agentic UX isn't a generic chat overlay. Instead, identify where users struggle with complex inputs like formulas or code. Replace these friction points with a native, natural language interface that directly integrates the AI into the core product workflow, making it feel seamless and powerful.
Cues uses 'Visual Context Engineering' to let users communicate intent without complex text prompts. By using a 2D canvas for sketches, graphs, and spatial arrangements of objects, users can express relationships and structure visually, which the AI interprets for more precise outputs.
The most effective application of AI isn't a visible chatbot feature. It's an invisible layer that intelligently removes friction from existing user workflows. Instead of creating new work for users (like prompt engineering), AI should simplify experiences, like automatically surfacing a 'pay bill' link without the user ever consciously 'using AI.'
AI development has evolved to where models can be directed using human-like language. Instead of complex prompt engineering or fine-tuning, developers can provide instructions, documentation, and context in plain English to guide the AI's behavior, democratizing access to sophisticated outcomes.
