Why Most Sora 2 Outputs Look Generic
If your Sora 2 videos keep coming out as good-looking but oddly generic clips, you are not alone. Most users write prompts the way they write ChatGPT prompts: a paragraph describing what they want, hoping the model figures out the rest. With Sora 2, that approach produces exactly what you would expect, technically competent video that lacks specific aesthetic intent.
The fix is structural, not creative. Sora 2 was designed to respond to prompts that look like a cinematographer's shot brief, not a creative writing prompt. Once you give it the structure it expects, the same idea, the same length, the same model, suddenly produces output that looks intentionally directed.
OpenAI's official Sora 2 prompting guide and the production teams at agencies that have shipped Sora-generated content publicly all converge on a similar framework. This article breaks down that framework into a structure you can apply to any prompt today, with a complete copy-paste template at the end.
What Is the Sora 2 Prompting Framework?
The Sora 2 prompting framework is a seven-section structured prompt format that mirrors how a film cinematographer briefs a shoot. Each section gives the model a specific layer of direction: what happens, how it looks, how it sounds, and what feeling it conveys. Together, they replace the vague paragraph approach with a shot-list that the model can execute precisely.
Sora 2, released by OpenAI in late 2025 with continuous prompting refinements through 2026, generates audio natively alongside video. This means audio direction is now part of the prompt, not a post-production step. Practitioners who treat sound as part of the prompt produce dramatically more coherent results than those who leave it to the model.
The seven sections are: Style, Scene, Photography, Lighting, Action, Dialogue, Sound. You do not need every section in every prompt, but treating these as your default mental checklist forces specificity into every shot.
How Do You Structure a Sora 2 Prompt That Actually Works?
Start every prompt with a one-line format declaration, then move section by section through the seven layers. The format declaration tells the model the overall rhythm to expect, and the section-by-section approach prevents you from leaving critical details to chance.
Format line: open with a phrase like "cinematic ad," "documentary B-roll," "performance music spot," or "social media short." This single phrase shapes every downstream choice the model makes about pacing, framing, and edit rhythm.
Style: specify the visual reference. Not "cinematic" alone, that is too vague. Use combinations like "shot on 35mm film with subtle grain," "Wong Kar-wai influenced colour grading," "early 2000s digital handheld documentary look."
Scene: where and when, in concrete physical terms. "A small ramen shop in Tsim Sha Tsui at 11pm, three customers at the counter, fluorescent lights from the kitchen behind, traffic noise outside." Specific physical details anchor the model.
Photography: camera and lens. "85mm prime, shallow depth of field, slight handheld movement," or "wide-angle 24mm, locked-off tripod, low angle." Real lens language produces real lens behaviour.
Lighting: direction, colour temperature, mood. "Practical lights from neon shop signs, tungsten warmth, deep shadows, single key light from frame left."
Action: described as beats, not as a paragraph. "Beat 1: subject sets down chopsticks. Beat 2: looks toward door. Beat 3: stands and exits frame right." One verb per beat. One camera move per beat.
Dialogue: if any, write it as a script with speaker tags. If none, say "no dialogue, ambient sound only" so the model does not invent voiceover.
Sound: ambient, foley, music. "Ambient: light rain on awning, distant traffic. Foley: chopsticks placed on ceramic bowl. Music: none."
What Does a Complete Sora 2 Prompt Look Like?
A complete prompt running through all seven sections is roughly 200 to 350 words. That is much longer than most users write, and the length is not the point, the structure is. The structure forces decisions the model would otherwise default to averages on, which is exactly what produces generic output.
Here is a copy-paste template you can adapt to any subject or use case:
Try This Prompt:
Format: cinematic ad, 8 seconds, single continuous shot.
Style: shot on 35mm film, fine grain, muted naturalistic colour palette in deep blue and warm amber, slight film halation around bright highlights. Influenced by [REFERENCE FILM OR DIRECTOR].
Scene: [SPECIFIC LOCATION], [TIME OF DAY], [WEATHER OR ENVIRONMENT]. [WHO IS PRESENT]. [WHAT IS HAPPENING IN THE BACKGROUND].
Photography: [LENS, e.g. 50mm prime], [APERTURE/DEPTH e.g. shallow depth of field, subject in sharp focus], [CAMERA MOVEMENT e.g. slow dolly in, locked-off, slight handheld].
Lighting: [PRIMARY LIGHT SOURCE], [COLOUR TEMPERATURE], [SHADOW QUALITY]. [ANY SECONDARY OR PRACTICAL LIGHTS].
Action:
--- Beat 1, 0 to 2 seconds: [SUBJECT ACTION], [CAMERA BEHAVIOUR].
--- Beat 2, 2 to 5 seconds: [SUBJECT ACTION], [CAMERA BEHAVIOUR].
--- Beat 3, 5 to 8 seconds: [SUBJECT ACTION], [CAMERA BEHAVIOUR].
Dialogue: [SCRIPT with speaker tags, OR "no dialogue, ambient only"].
Sound:
--- Ambient: [BACKGROUND SOUND].
--- Foley: [SPECIFIC ACTION SOUNDS].
--- Music: [GENRE AND MOOD, OR "none"].
End frame: [DESCRIPTION OF FINAL VISUAL].
The end frame instruction is the underrated detail. Telling the model exactly where the shot lands gives it a target to compose toward, which significantly improves the closing seconds of the clip.
How Do You Get Consistent Results Across Multiple Shots?
Consistency across a multi-shot piece is the single hardest part of working with Sora 2. The model has no memory between generations, so each prompt must independently re-establish the visual world. Practitioners who ship multi-shot Sora content do this by maintaining a separate "world bible" prompt block they paste into the top of every shot prompt.
The world bible covers the constants: visual style, colour palette, characters, primary location, time of day, lighting setup. Anything that should not change between shots goes here. The shot-specific prompt then describes only what is new: the action, the camera angle, the framing.
For character consistency specifically, lock down the description down to the specific level: "Asian woman, late 30s, shoulder-length black hair tied back, navy linen blazer over white t-shirt, small silver pendant necklace, no other jewellery." The model interpolates from your description, so vague descriptions produce a different person each time.
Use the image-to-video feature when you absolutely need character consistency. Generate one strong reference frame, then use it as the starting image for multiple shots. This is more reliable than describing the character in text alone, especially for projects where the same person appears in three or more scenes.
Where Does Sora 2 Still Fall Apart?
Sora 2 is genuinely impressive but it has clear failure modes you need to know about before committing to a use case. The three most common issues are physical realism in complex actions, hands and small objects, and dialogue lip sync.
Physical realism breaks down in actions with multiple interacting objects, pouring liquid into a glass that someone is holding, throwing a ball that someone catches, two characters shaking hands. The model often produces visible artefacts or inconsistencies in the moment of contact. For ad work, design around this: cut away from the contact moment, use single-actor scenes, or accept the artefact and re-roll.
Hands remain the model's weakest area. Closeups on hands holding small objects, typing, or doing precise actions will frequently produce visible warping. Either avoid hand-focused shots, frame them so the hands are partially obscured, or be prepared to generate many takes to find the clean one.
Dialogue lip sync is improving but still inconsistent. Sora 2 generates audio natively, which is great, but the lip movement does not always match the words convincingly. For dialogue-heavy work, voiceover with a wide or back-of-shot composition works better than tight talking-head closeups.
How Should You Iterate When the First Output Is Not Right?
Iteration with Sora 2 is not the same as iteration with text models. You cannot ask it to "make the lighting moodier in the second half" mid-clip. Each generation is fresh. The right iteration approach is structured editing of the source prompt, not conversational refinement.
The pattern that works: identify the single biggest issue with the output, then change the smallest possible part of the prompt to address it. If the lighting feels too flat, do not rewrite the entire prompt, edit only the lighting section to add a stronger key direction or a colder shadow tone. Then regenerate.
Track which sections of your prompt produced which behaviours by keeping a simple log: prompt version, what changed, what improved, what got worse. After 10 to 20 generations on a project, you will have learned how Sora 2 specifically interprets your style and subject combinations, which is the actual durable skill, not the prompts themselves.
The teams who produce consistent Sora 2 output do not have secret prompts. They have prompt templates they have tuned for their specific aesthetic over dozens of iterations. Build your own template by starting with the structure above and refining it for the kind of work you do most often.
Try It Now: A Single-Shot Test
Pick any subject from your work this week, a product, a location, a person, an object. Write a complete seven-section prompt for an 8-second cinematic shot of that subject. Then write a vague paragraph version of the same idea. Generate both. Compare.
The structured version will not always be better, sometimes the vague version stumbles into something accidentally good. But the structured version will be reliably specific in a way the vague version cannot match. Over a project, that reliability is what separates output you can ship from output you have to keep re-rolling.
Once you find a structured prompt that works for your subject, save it. Build a small library of prompt templates for the kinds of shots you produce regularly. The compounding value of a prompt library is the real productivity unlock with these tools, not the individual prompt.
The people getting the most out of AI video right now are not the ones writing the cleverest single prompts. They are the ones who have built repeatable workflows around the tool, treating it like any other production system: structured input, predictable output, refined over iterations. UD has been helping Hong Kong businesses turn new technology into reliable workflows for 28 years. 懂AI，更懂你。UD相伴，AI不冷。
Build AI Tools Into a Workflow That Works Every Time
Knowing the prompt framework is the first step. Building it into a repeatable production workflow that ships consistent results is the next. We'll walk you through every step, from prompt template design to platform setup to scaling output across your team.
