How to Write Prompts for Sora 2: The Spec That Turns "Cool Video" Into Something You Can Ship

You can tell who's new to video models because they prompt Sora 2 like it's an image generator: a dense pile of style words and vibes, followed by "cinematic" twice for luck.

It works… until it doesn't. Then you get the classic failure mode: the first output is close, the next run drifts, continuity breaks, the camera does something you didn't ask for, and the "main character" quietly becomes a different person.

Here's the take that actually holds up in practice: for Sora 2, your prompt is not a caption. It's a spec. And the fastest way to get reliable results is to structure that spec so the model can keep a stable plan across turns and revisions-because multi-turn drift is real, and every additional request grows the "prompt surface area" the system has to manage [1].

---

The mental model: prompts are evolving prefixes, not one-offs

When people complain that "the model isn't consistent," what they often mean is: "I'm changing my instructions in ways that destroy prefix stability."

OpenAI's engineering write-up on the Codex agent loop makes a point that matters beyond coding: as you iterate, the new prompt tends to be an exact prefix of the old prompt plus new information, and systems get real performance and behavior benefits when the stable, earlier part stays stable [1]. That's literally why prompt caching works: "exact prefix matches" are the unit of reuse [1].

For Sora 2 prompting, you want the same shape, even if you're not using an API. Put the stuff you don't want to renegotiate-your non-negotiables-up front. Then append deltas at the end when you iterate: "change only X, keep everything else."

This seems small, but it's the difference between steering and re-rolling.

---

What to put in the "stable prefix" for Sora 2

When I'm writing Sora 2 prompts, I aim for a stable prefix that answers four questions, in roughly this order:

First, what are we making, in plain language? This is the one-sentence premise. If you can't write it, you don't have a brief.

Second, what must remain true across the whole clip? This is where you pin continuity. Character identity, wardrobe, props, environment, time of day, and the "physics" you want (or don't want). If you're a PM, think of these as acceptance criteria.

Third, what is the camera doing? Video is camera + motion. If you don't specify camera behavior, you're implicitly letting the model choose. Sometimes that's fine. Often it's the reason your output feels "random." So I call out shot type, lens feel, movement, and pacing.

Fourth, what is the aesthetic, but as constraints not adjectives? "Cyberpunk" is not a constraint. "Neon signage, wet asphalt reflections, high contrast, shallow depth of field" is closer to something the model can operationalize. You're trading vibes for observable details.

If you do only one thing after reading this, do this: replace half your adjectives with concrete, visual facts.

---

Iteration without drift: append changes, don't rewrite the whole prompt

The OpenAI "Inside GPT-5 for Work" report is mostly about workplace adoption, but there's a useful signal hidden in there: early usage patterns are dominated by writing/research/programming/analysis, and advanced features are used most by technical teams who naturally work in iterative loops [2]. People who get value from AI systems tend to treat them like iterative tools, not magic boxes.

For Sora 2, iteration discipline is everything. My rule is: never rewrite from scratch unless the concept changed.

Instead, I keep a "master prompt" and append a revision block at the end that reads like a git commit message: what to change, what to preserve, and what to avoid. This mirrors how agent systems try to preserve context and avoid "compaction" or losing details as prompts grow [1].

That revision block is also where you can do targeted debugging: "the character's jacket color drifted-lock it to red leather," or "reduce camera shake," or "no text overlays."

---

Practical examples (prompts you can copy and adapt)

Below are three prompts written in that "spec first" style. They're intentionally not bloated. Short can be strong, as long as the constraints are crisp.

Example 1: Product shot with controlled camera

Create a 6-8 second video.

Premise: A close-up product shot of a matte-black smart ring on a ceramic pedestal in a minimalist studio.

Continuity constraints:
- Only one object: the ring. No extra jewelry.
- Studio background: off-white seamless, no visible seams.
- Lighting: soft key from camera-left, subtle rim light from behind, no harsh shadows.
- No text, logos, or UI overlays.

Camera + motion:
- Start: macro close-up on the ring's surface texture.
- Slow clockwise orbit around the ring, 20-30 degrees total.
- End: gentle rack focus from the near edge of the ring to the far edge.

Look:
- Clean, modern, premium.
- Natural color, low saturation, shallow depth of field.

Example 2: Character continuity + action

Create an 8-10 second video.

Premise: A woman in a red leather jacket walks through a rainy night street market, weaving between stalls.

Continuity constraints:
- Same character throughout: mid-30s, short black hair, red leather jacket, dark jeans, black boots.
- Location: night street market, narrow walkway, wet ground, neon signage, light rain.
- Keep her jacket red in every shot. No outfit changes.

Camera + motion:
- Tracking shot from behind at waist height, steady, no handheld shake.
- She turns her head once to glance left at a stall, then continues forward.
- Background crowd remains soft and out of focus.

Look:
- High contrast, neon reflections on wet asphalt, shallow depth of field.
- No subtitles or text.

Example 3: Iteration block to fix a specific failure

This is how I'd revise Example 2 without redoing the whole prompt:

REVISION (apply changes only; keep everything else the same):
- Reduce the rain intensity by 50%.
- Make the neon signage less dominant; keep it present but not blown out.
- Keep the camera perfectly stabilized (no micro-jitters).
- Ensure the character's red leather jacket stays consistently red (no maroon/brown shifts).

That last line looks redundant. It isn't. Redundancy on identity constraints is often what prevents drift.

---

What real users get right (and wrong)

A pattern I see in community discussions is that people don't actually want "better prompts." They want prompts that don't degrade when they iterate-prompts that behave like controllable systems.

One Reddit thread frames this as "prompts are fragile" and argues for a "prompt system" with explicit constraints to stop drift over multiple turns [3]. I agree with the framing. Where I disagree is the temptation to fix fragility by making the system message enormous. Bigger isn't automatically better; clearer invariants are better.

So yes: treat your Sora 2 prompt like a system. But keep it tight. Stable prefix + small deltas.

---

Closing thought: write the spec you wish your model could infer

If you prompt Sora 2 with vibes, you'll get vibes back-with all the instability that implies. If you prompt it with a spec, you'll get something you can iterate toward production.

The next time you're stuck, don't ask "what words make it cinematic?" Ask "what observable facts would convince me this is the scene I want?" Then put those facts at the top of the prompt, and only append changes at the bottom.

That's the whole game.

---

References

References
Documentation & Research

1. Unrolling the Codex agent loop - OpenAI Blog: https://openai.com/index/unrolling-the-codex-agent-loop
2. ChatGPT usage and adoption patterns at work - OpenAI Blog: https://openai.com/business/guides-and-resources/chatgpt-usage-and-adoption-patterns-at-work

Community Examples
3. Building a prompt system that's controllable under the hood, looking for 5 power users to stress-test it - r/ChatGPTPromptGenius: https://www.reddit.com/r/ChatGPTPromptGenius/comments/1qiqbfi/building_a_prompt_system_thats_controllable_under/