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.

A powerful workflow is to explicitly instruct your AI to act as a collaborative thinking partner—asking questions and organizing thoughts—while strictly forbidding it from creating final artifacts. This separates the crucial thinking phase from the generative phase, leading to better outcomes.

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.

AI tools rarely produce perfect results initially. The user's critical role is to serve as a creative director, not just an operator. This means iteratively refining prompts, demanding better scripts, and correcting logical flaws in the output to avoid generic, low-quality content.

The challenge in using AI effectively is often prompt engineering, not model capability. A potential solution is a social platform where users can follow experts, discover their prompts, and be 'catalyzed' by others' creativity. This democratizes access to AI's full potential beyond one's own ingenuity.

Exceptional AI content comes not from mastering one tool, but from orchestrating a workflow of specialized models for research, image generation, voice synthesis, and video creation. AI agent platforms automate this complex process, yielding results far beyond what a single tool can achieve.

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.

Genspark's 'auto prompt' function takes a simple user request and automatically rewrites it into more detailed, optimized prompts for different underlying image and video models. This bridges the gap between simple user intent and the complex commands required for high-quality generative AI output.