Sora 2 Prompt Generator & Prompt Engineering Reference

Prompt engineering is the practice of specifying inputs to generative models so that the resulting output is relevant, consistent, and useful. In applied contexts the activity includes goal definition, constraint selection, and the use of structured formats. Prompt engineering is commonly associated with AI prompting for large language models, image synthesis systems, and text-to-video models such as those colloquially referred to as Sora 2. [Wikipedia]

AI Prompting

AI prompting denotes the act of providing instructions to a model. In the context of language and multimedia systems, a prompt can include a description of the desired content, formatting constraints, style markers, or examples. Well-specified prompts decrease ambiguity, which tends to improve reproducibility and downstream reliability. For video generation, prompts frequently describe not only visible elements but also the perspective from which the scene is observed (for example, the camera position or motion). See OpenAI Prompt Engineering Guide.

Prompt Engineering

Prompt engineering refers to systematic techniques for composing prompts. The activity commonly employs: (i) decomposition of a task into sub-tasks; (ii) explicit declarations of constraints; (iii) few-shot demonstrations; and (iv) structured inputs such as key-value pairs. Professional roles sometimes titled “AI prompt engineer” apply these methods to align outputs with editorial, scientific, or production requirements.

• Specificity. Descriptions of scene, action, camera, lighting, and mood reduce variance.
• Exclusions. Unwanted artifacts may be reduced when the prompt lists elements to avoid (for example, “no text overlays”).
• Iteration. Outputs are evaluated and prompts are revised in small steps to converge on a target style or behavior.

Text-to-Video Prompting

Text-to-video prompting is the specification of moving image content through natural-language descriptions. Prompts typically include subject matter, environment, temporal cues, and cinematographic information. The latter may reference lens length, shot type, camera motion, and depth-of-field. Because video models represent change over time, instructions about rhythm, pacing, and transitions are often included. [Wikipedia]

Example prompt snippet: Medium close-up of a courier at night in a neon-lit alley; handheld dolly-in; shallow depth-of-field; cool rim light; light drizzle on asphalt.

Sora 2 Prompting

Sora 2 is a commonly used label for advanced text-to-video systems that synthesize short video clips from natural language. In a prompt-engineering context, “Sora 2 prompting” denotes writing inputs intended to guide such systems. A typical Sora 2 prompt (or Sora prompt) specifies visual tone, motion, perspective, and environment. Some creators maintain a prompt generator or prompt guide to standardize phrasing across projects. [OpenAI Sora]

• Scene. Place, time, weather, textures.
• Subject and action. Characters and behaviors.
• Camera. Lens, shot, and movement (for example, tracking shot or slow pan). See Cinematography.
• Lighting. Key, fill, color temperature, and reflections. Learn three-point lighting.
• Optional audio. Ambient or diegetic sounds described at a high level.
• Exclusions. Elements to avoid, such as Dutch angles or text overlays.

Sora 2 Prompt Generator

This section provides a minimal, framework-free form that assembles a structured string suitable for experimentation with text-to-video models and tools labeled as Sora 2. The generator is intentionally simple and remains compatible with low-bandwidth or script-limited environments.

Output optimized for Sora 2. Use --ar 16:9 for standard video.

Best Practices

• Decompose the task. Provide independent clauses for setting, action, camera, lighting, and exclusions.
• Prefer concrete nouns and verbs. Ambiguous adjectives (for example, “beautiful”) can be replaced with measurable qualities (for example, “soft key light” or “wide-angle establishing shot”).
• Iterate with evidence. Adjust the prompt in response to observed artifacts rather than speculative failure modes.
• Maintain a log. A short table recording prompt versions and outcomes supports reproducibility.
• Use aspect ratio flags. Append --ar 16:9 or --ar 9:16 for consistency.

Structured Prompting

Structured prompting is the use of machine-readable formats—such as JSON, YAML, or simple key-value pairs—to reduce ambiguity. The technique is useful when the desired output must conform to a known schema or when multiple prompts will be generated programmatically. [arXiv: Structured Prompting]

{"scene":"neon alley at night, light drizzle","subject":"courier adjusts helmet, breath visible","camera":"35mm, subtle dolly-in, shallow DOF","lighting":"practical neons as key; cool rim","exclude":["text overlays","dutch angles"]}

Frequently Asked Questions

See Also

• Prompt engineering (Wikipedia)
• OpenAI Prompt Engineering Guide
• Learn Prompting — Video Generation
• AI prompting
• Prompt engineering
• Text-to-video
• Prompt generator