Lesson Content

Atmospheric and environment scenes.

For environment and atmospheric scenes (landscapes, weather, time-lapse-style content), prioritize: environmental specificity (precise location type, time of day, weather, season), atmospheric lighting description (golden hour, overcast diffuse light, dramatic storm lighting), and camera/motion approach (static establishing shot, slow pull back, drone-perspective sweep). These elements guide the model toward the environmental atmosphere rather than defaulting to a generic interpretation.

Describing specific subject behavior.

For content centered on a specific subject's behavior (a character performing an action, an animal in motion), prioritize precise behavioral description – what exactly is the subject doing, how fast, in what direction, in what context. "A red fox leaping across a snow-covered field, slow motion, low-angle close-up perspective" is more effective than "a fox in snow" for achieving a specific subject behavior result.

Abstract and experimental generation.

For abstract or non-representational visual content, prompts can lean into texture, color, movement, and mood rather than literal subject description – "swirling deep blue and violet fluid textures, slow organic morphing motion, dreamlike quality" vs. trying to describe a literal scene that doesn't exist. This approach works well for backgrounds, visual effects elements, and experimental art content.

Stylized vs. photorealistic targeting.

When targeting photorealistic output, add photography and cinema descriptors (shot on film, 35mm lens, natural lighting, documentary style). For stylized output, add the specific style name and artistic movement descriptors (Art Nouveau illustration style, watercolor animation, Studio Ghibli aesthetic). The model responds significantly to these framing cues.