How to Write Prompts That Work for Sora 2

Structuring Prompts for Sora 2

OpenAI's Sora 2 produces output quality almost entirely determined by prompt structure. Developers who approach it like a conversational interface generate generic results. Research on text-to-video diffusion models confirms that detailed, video-centric prompts significantly outperform simple user inputs, which often fail to capture the dynamic requirements of motion, temporal coherence, and scene transitions.¹

For production integration, Sora 2 via fal provides API access with webhook support and queue management suitable for automated pipelines. OpenAI's native interface through ChatGPT remains preferable for prototyping or learning prompt structure, as it includes built-in refinement tools. Skip Sora 2 entirely if you require real-time generation (expect multi-minute queue times under load), frame-level control, or technically accurate depictions of specialized subject matter.

The Prompt Framework

Effective prompts address three fundamental questions: what is the shot, how is it framed, and what is the visual style?

Shot Description: Begin with camera angle and movement. Phrases like "a low-angle tracking shot" or "an overhead drone shot descending" establish visual perspective immediately.

Subject and Action: Define the scene with precision. Rather than "a person walking," specify "a woman in a red coat walking briskly through a crowded market, weaving between vegetable stalls."

Visual Style and Atmosphere: Control the aesthetic through explicit references. "Shot on 35mm film with shallow depth of field" or "cinematic lighting with warm golden hour tones" provides concrete direction.

OpenAI's official prompting guide emphasizes treating each prompt as a storyboard panel, briefing the model as you would a cinematographer.²

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Comparative Prompt Analysis

Weak Prompt	Strong Prompt	Key Improvements
A cat playing with a ball	Close-up shot of a tabby cat batting a red yarn ball across hardwood floors, shallow depth of field with background bokeh, natural window light creating soft shadows, shot on 35mm film	Framing, subject details, depth of field, lighting, visual style
City street at night	Wide-angle shot slowly pushing forward down a rain-soaked Tokyo street, neon signs reflecting in puddles, bokeh from car headlights, cinematic teal and orange grading	Camera movement, environmental detail, color direction
Product spinning	Smooth 360-degree rotating shot of black wireless headphones on a white pedestal, studio lighting with soft shadows, minimal background, commercial photography style	Motion specificity, lighting setup, stylistic reference

API Integration on fal

The fal Sora 2 endpoint supports queue-based generation with webhook callbacks. Check the model page for current pricing, which varies by duration and resolution.

Basic request submission:

import fal_client

result = fal_client.subscribe(
    "fal-ai/sora-2/text-to-video",
    arguments={
        "prompt": "Close-up of rain drops hitting a window, bokeh city lights behind, melancholic blue tones",
        "duration": 4,
        "aspect_ratio": "16:9"
    }
)
print(result["video"]["url"])

For production systems, use fal.queue.submit() with a webhook URL rather than blocking on subscribe(). The response includes a request_id for status polling. See the Queue documentation for retry patterns and error handling.

Cinematographic Controls

Camera Movement Vocabulary:

Tracking shot: follows subject laterally
Dolly zoom: simultaneous zoom and dolly movement
Crane shot: vertical camera movement
Handheld: documentary-style texture
Steadicam: smooth cinematic quality

Depth of Field: "Shallow depth of field with subject in sharp focus and background bokeh" versus "deep focus with foreground and background elements sharp."

Lighting Direction: Reference established techniques such as "Rembrandt lighting with strong key light from the left," "soft diffused lighting from above," or "dramatic side lighting creating strong shadows."

Temporal Structure: Break action into clear stages. "A paper airplane being thrown, gliding smoothly through the air, then landing gently on a desk" provides sequential waypoints.

Equipment References: "Shot on Arri Alexa with anamorphic lenses" or "16mm documentary style" leverages visual patterns encoded during training.

Model Constraints and Workarounds

Limitation	Impact	Workaround
Physics consistency	Liquids, cloth, multi-object collisions may behave implausibly	Simplify interactions; "silk scarf in gentle breeze" succeeds where "scarves tangling in storm" fails
Text rendering	On-screen text appears garbled	Add text in post-production
Frame-level control	Cannot specify "turn left at 3 seconds"	Use traditional editing for precise timing
Temporal coherence	Videos beyond 5-10 seconds show consistency drift	Generate shorter clips and composite; consider other models for longer sequences
Subject accuracy	Technical content (medical, machinery) may be plausible but incorrect	Verify critical details; do not use for instructional content requiring precision
Default camera motion	Model adds subtle movement even when unwanted	Explicitly state "static shot" or "locked-off camera"

Troubleshooting Common Issues

Generic output despite detailed prompt: Add specific equipment references. "Shot on Kodak Vision3 500T film stock with natural grain" outperforms vague terms like "cinematic."

Subject morphing mid-clip: Reduce prompt complexity. Generate simpler shots and composite them.

Wrong aspect ratio: Always specify in the API call. Cropping wastes resolution.

Pacing problems: Use temporal modifiers explicitly: "slowly," "gradually," "sudden."

Vague action: Replace imprecise verbs. "Sprinting," "strolling," or "tiptoeing" outperforms "moving."

Access Method Comparison

Feature	OpenAI Direct	fal API
Access Method	Web interface via ChatGPT	REST API with webhooks
Best For	Learning, prototyping	Production pipelines, automation
Batch Processing	Manual	Programmatic with queue management
CI/CD Integration	Not supported	Native support via API
Cost Model	Subscription-based	Per-generation; check model page for current rates

Production Workflow

Script shots in advance: Write a shot list before generating. This forces clarity and reduces iteration waste.
Run test generations: Generate variations of critical shots with slightly different prompts to identify optimal approaches.
Refine incrementally: Small adjustments to successful prompts yield better results than complete rewrites of failed ones.
Generate shorter clips: Even for longer content, produce 5-10 second segments and edit together for greater control.
Build template libraries: Create reusable templates for common shot types: product rotations, landscape establishing shots, close-up details.

Summary

Sora 2 output quality correlates directly with prompt specificity. The fal API enables production workflows with queue management, webhook callbacks, and programmatic batch processing that the ChatGPT interface cannot provide.