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Creative AI Updated 2026-07-05

Runway Video Prompt Prompt Template and Example

Use this Runway Video Prompt prompt template when you want a structured AI answer instead of a loose request. The guide combines the reusable prompt, a concrete example, and links to nearby templates so the page stays useful rather than being a thin keyword page. Write text-to-video prompts with subject motion, camera movement, scene, and duration.

Open Runway Video Prompt in the editor

Reusable prompt

Create video prompts that describe motion clearly enough for vertical video AI tools.

Task type: Runway Video Prompt
Objective: Create video prompts that describe motion clearly enough for vertical video AI tools.

Context:
- [Project, product, or topic]: [Project, product, or topic]
- [Audience and situation]: [Audience and situation]
- [Constraints, must-haves, and things to avoid]: [Constraints, must-haves, and things to avoid]

Inputs to provide:
[Paste source material here]

Expected output:
1. Scene outline
2. Visual direction
3. Camera movement
4. Motion notes
5. Negative prompt

Quality bar:
- Be specific and avoid generic advice.
- State assumptions explicitly.
- Prefer actionable next steps over broad theory.
- If important information is missing, ask up to 3 clarifying questions before answering.
- For time-sensitive or factual claims, label what is known, inferred, and needs verification.

Worked example

The example below fills the same prompt for a realistic Creative AI scenario. It is intentionally modest: the goal is to show how the prompt behaves, not to pretend one template solves every Creative AI problem.

Task type: Runway Video Prompt
Objective: Create video prompts that describe motion clearly enough for vertical video AI tools.

Context:
- [Project, product, or topic]: A real Creative AI task using the Runway Video Prompt prompt
- [Audience and situation]: A teammate who needs a useful answer and clear next steps
- [Constraints, must-haves, and things to avoid]: Be specific, state assumptions, avoid unsupported claims, and keep the output easy to act on.

Inputs to provide:
Sample material: The team needs help with Runway Video Prompt. The current situation is messy, the goal is clear enough to start, and the answer should separate facts, assumptions, risks, and next actions.

Expected output:
1. Scene outline
2. Visual direction
3. Camera movement
4. Motion notes
5. Negative prompt

Quality bar:
- Be specific and avoid generic advice.
- State assumptions explicitly.
- Prefer actionable next steps over broad theory.
- If important information is missing, ask up to 3 clarifying questions before answering.
- For time-sensitive or factual claims, label what is known, inferred, and needs verification.

How to use this prompt

  1. Replace the placeholders with the actual Runway Video Prompt task, audience, source material, and constraints.
  2. Keep the requested output sections unless you have a strong reason to remove one; they are there to make the AI answer easier to evaluate.
  3. Paste the finished prompt into your AI assistant, then ask one follow-up question that tests assumptions or missing evidence.

What a good answer should contain

  • 1. Scene outlineUse this section to make the answer concrete: Scene outline.
  • 2. Visual directionUse this section to make the answer concrete: Visual direction.
  • 3. Camera movementUse this section to make the answer concrete: Camera movement.
  • 4. Motion notesUse this section to make the answer concrete: Motion notes.
  • 5. Negative promptUse this section to make the answer concrete: Negative prompt.

Why this prompt works

  • Runway Video Prompt starts with an explicit task type and objective, which reduces vague answers.
  • It asks for context, source material, and constraints before the model writes the final response.
  • The 5 output sections make the answer scannable and easier to compare across attempts.
  • The quality bar tells the assistant to ask clarifying questions and mark claims that need verification.

Common mistakes to avoid

  • Leaving placeholders untouched and expecting the model to infer the missing context.
  • Removing the output structure, then asking for a final answer that is hard to review.
  • Using the prompt for time-sensitive facts without checking sources or dates.