AI Video Prompt Guide: How to Write Prompts That Actually Work

What makes an AI video prompt effective

A prompt for AI video generation works when it describes what happens, not just what is visible. Video models like Sora, Kling, Runway Gen-3, Veo and Pika respond to sentences containing four elements: a clear subject, a specific action, a defined camera movement and a recognizable visual style. Leaving out even one produces generic, shaky or incoherent clips.

The basic rule: the more precisely the movement is described, the more stable the result. A video model doesn't interpret atmosphere the way a text model does: it interprets verbs, directions and speeds.

The 5-block prompt structure

An effective video prompt almost always follows this order, because models weigh the opening words more heavily:

1. Subject: who or what appears, with concrete physical details (“a woman in her thirties with red hair and a beige coat”).
2. Action: one main movement per clip (“walks slowly toward the camera”). Multiple actions in a single prompt generate artifacts.
3. Camera: shot type and movement (see next section).
4. Environment and light: location, time of day, type of lighting (“wet street at sunset, warm low-angle light”).
5. Style: reference aesthetic (“cinematic 35mm”, “documentary”, “90s anime”).

Write the prompt like a clapperboard caption: subject, action, camera, light, lens. If a director couldn't shoot it as described, the model won't generate it well.

Camera language: the vocabulary that really matters

Camera movement is the block where most prompts fail. Use film terminology, because models are trained on data labeled with these terms:

Always add speed: “slow”, “gentle”, “fast” are words the models respect. A “dolly-in” without an adjective often produces movement too abrupt for a 5-10 second clip.

Differences between models: how to adapt the same prompt

There is no universal prompt. Every model has its own biases and preferred syntax:

• Sora and Veo: prefer long descriptive prompts, almost narrative paragraphs. They handle light and atmosphere details well.
• Runway Gen-3: works best with leaner prompts, subject + action + camera. Overly long descriptions dilute the action.
• Kling: very sensitive to motion verbs and facial expressions; excellent for complex physical actions.
• Pika and lighter models: better to use one strong concept per clip, without complex subordinates.

The right strategy is to write a neutral master prompt, then adapt it: expand it into narration for Sora, strip it to the bone for Runway. Test the same concept on two models before discarding an idea: often the prompt isn't wrong, it's just incompatible with that model.

Practical workflow: from concept to usable clip

1. Define one action per clip: 5-10 seconds of video can hold one movement. If you need more actions, plan more clips.
2. Write the master prompt with the 5 blocks in order.
3. Generate 3-4 variants without changing the prompt: assess the model's natural variance before making corrections.
4. Diagnose by symptom: deformed subject → simplify the action; random camera → use “static shot”; inconsistent style → add explicit references (“shot on 35mm film, shallow depth of field”).
5. Iterate one block at a time: change the camera, or the light, or the action. Changing everything at once makes it impossible to know what improved or worsened the result.
6. Lock the seed when the model allows it, to maintain consistency across iterations.

Common mistakes to avoid

• Long negative prompts: a short “no text, no distortion” beats a list of ten exclusions.
• Compound actions: “stands up, picks up the cup and smiles” almost always produces wrong limbs. Split it into three clips.
• Impossible physics: models struggle with liquids, hands manipulating objects and complex reflections. Describe actions the dataset's physics can support.
• On-screen text: asking for titles or writing inside the video almost always produces illegible characters. Add text in post-production.
• Ignoring the first frame: when the model allows it, use a starting image (image-to-video). A good first frame cuts the prompt work in half.

Key takeaways

An effective AI video prompt isn't poetry: it's a technical specification. Subject, single action, explicit camera, light and style: these five blocks, in the right order, are worth more than any evocative adjective. Iterate one block at a time, adapt prompt length to the model you're using, and leverage image-to-video whenever you can. Those who master camera language and symptom-based diagnosis produce usable clips in 2-3 iterations instead of ten.