Video is changing. Fast. If you’ve been following the trajectory of generative AI, you know the jump from early "spaghetti-eating" memes to what we’re seeing now is nothing short of a fever dream. OpenAI’s evolution has led us straight to the ChatGPT Sora 2 prompt era, where the difference between a cinematic masterpiece and a digital hallucination comes down to how you talk to the machine.
It’s weird.
Most people treat these prompts like a Google search. They type in "cool sunset over ocean" and wonder why the result looks like a stock screensaver from 2005. The reality is that Sora 2—the internal shorthand many are using for the refined, more controllable version of OpenAI's video model—requires a totally different brain space. You aren't just describing a scene; you are directing a physics engine that understands light, weight, and the way fabric catches the wind.
The Physics of a ChatGPT Sora 2 Prompt
Think about gravity for a second. In early AI video, people didn't have weight. They sort of drifted across the floor like ghosts. When you’re crafting a ChatGPT Sora 2 prompt, you have to explicitly or implicitly account for the "grounding" of the scene.
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OpenAI researchers like Bill Peebles and Tim Brooks have often pointed out that Sora treats video generation as a "world simulator" rather than just a sequence of frames. This means the model is trying to calculate how 3D objects interact in a 2D space. If you want a video of a glass breaking, you can't just say "glass breaks." You need to describe the impact. The velocity. The way the shards skitter across the hardwood.
It's about the "why" and the "how," not just the "what."
Honestly, the most successful prompts I’ve seen lately aren't the ones that use fancy adjectives. They are the ones that use technical cinematography terms. If you tell the AI to use a "dolly zoom," it understands the mathematical relationship between the lens focal length and the camera’s movement. That’s a huge level up from just saying "zoom in."
Why Specificity Beats Hype
Every single time someone complains that their AI video looks "uncanny," it’s usually because the lighting is inconsistent. Light is hard. In a ChatGPT Sora 2 prompt, describing the light source is the single most important thing you can do to avoid that "AI look."
Is it golden hour? Is the light being diffused by a heavy morning fog? Maybe it’s the harsh, flickering neon of a Tokyo back alley reflected in a puddle of rainwater? When you give the model those environmental cues, it stops guessing. It starts simulating.
Breaking the "Prompt Engineering" Myth
There's this idea that you need a 500-word essay to get a good result. That's actually not true. Sometimes, the model gets "lost" in the sauce if you give it too much conflicting information. You want a lean, muscular prompt.
- Start with the core action. Who is doing what?
- Move to the camera movement. Is it a wide shot? A close-up? A handheld shaky cam?
- Add the environmental texture. This is where you mention the "grit" or the "gloss."
- Define the lighting. This is the "soul" of the video.
Most people skip the camera movement. They just describe the person. But if the camera is static, the video feels dead. By adding "low angle, slow tracking shot," you suddenly give the scene a sense of scale and drama that a static description simply cannot achieve.
The Problem with "Photorealistic"
Stop using the word "photorealistic." Please.
It’s a junk word. It doesn’t mean anything to the model anymore because it’s been overused in millions of low-quality training sets. Instead, talk about the camera gear. Mention "shot on 35mm film" or "captured on a Sony A7S III with a 50mm prime lens." Even if the AI isn't literally "using" that lens, it understands the aesthetic characteristics associated with those keywords—the shallow depth of field, the grain, the color science.
It’s a shortcut to quality.
Real Examples of the ChatGPT Sora 2 Prompt Shift
Let's look at a "before and after" scenario.
- The Amateur Prompt: A futuristic city with flying cars and neon lights, high resolution, 4k, cinematic.
- The Sora 2 Style Prompt: A high-speed tracking shot through a rain-slicked cyberpunk street. The camera follows a hovering vehicle at eye level. Neon signs in Mandarin reflect off the wet asphalt. Intense motion blur on the edges of the frame. Shot on anamorphic lenses with blue and orange teal color grading.
See the difference? The second one gives the AI a "logic" to follow. It knows it needs to render reflections (wet asphalt), it knows how to handle the edges (motion blur), and it knows the color palette (teal and orange).
It’s a blueprint, not a wish list.
Handling the "Glitch"
We have to be honest: Sora isn't perfect. It still struggles with complex transformations. If you ask for a man turning into a bird, it’s probably going to look like a nightmare. The current sweet spot for a ChatGPT Sora 2 prompt is realistic human movement and environmental interaction.
The "digital puppetry" aspect is getting better, but if you push the physics too far, the model snaps. I’ve found that sticking to "grounded" movements—walking, sitting, talking, pouring liquid—yields the most mind-blowing results.
The magic happens in the mundane.
The Ethics and Limitations Nobody Talks About
We can't talk about Sora without mentioning the guardrails. OpenAI has been very vocal about their Red Teaming efforts. If you try to prompt for specific public figures or "not safe for work" content, the system will just bounce it.
There is also the "watermark" issue. Every video generated has metadata and visual markers to identify it as AI-generated. This is a huge talking point in the industry right now, especially with the C2PA standards becoming the norm. When you write a ChatGPT Sora 2 prompt, you are working within a "safe" sandbox.
Some people find this stifling. Others see it as a necessary step for the tech to be adopted by big studios. Regardless of where you stand, the limitations are part of the tool. Learning to prompt around the "forbidden" zones is a skill in itself.
The Role of Audio
One of the coolest things about the latest iterations is the integration of sound. We’re moving toward a world where the prompt doesn't just generate the visual; it generates the foley.
Imagine typing: "A heavy velvet curtain closing in an empty theater."
In a true Sora 2 environment, you’re not just looking for the visual of the fabric folding; you’re looking for that low, muffled "whoosh" of heavy material. We aren't quite at "perfect" text-to-video-to-audio sync yet, but the prompts are starting to reflect that expectation.
Practical Steps for Mastering Your Next Video
If you want to actually use this for work—whether it’s for a mood board, a social ad, or just experimenting—you need a workflow. You can't just wing it.
- Use ChatGPT as a Co-Director: Don't write the prompt alone. Ask ChatGPT to "expand this scene description for a high-end cinema camera." Let it help you with the technical jargon.
- Focus on the First Frame: Sora uses a "diffusion" process. The starting point matters. If you can describe the initial composition clearly, the rest of the motion follows a more logical path.
- Iterate on Lighting First: If the video looks "off," don't change the action. Change the light. Switch from "midday sun" to "overcast" and watch how the skin textures suddenly look 10x more realistic.
- Study Real Film: Go watch a movie by Denis Villeneuve or Roger Deakins. Look at how they frame shots. Use those descriptions in your prompts. "Chiaroscuro lighting" or "minimalist composition" goes a long way.
The era of "guessing" what the AI will do is ending. We are moving into the era of "intent." The more you understand how a real camera works, the better your ChatGPT Sora 2 prompt results will be. It’s a tool for creators who understand the craft of image-making, not just those who can type fast.
Start by taking a scene from your favorite book. Don't describe the plot. Describe the camera angle, the dust motes dancing in the light, and the way the character's hand trembles. That’s where the "human" quality comes from. That’s how you win.