In recent years, AI-powered tools have made it possible to turn simple text prompts into full-fledged video clips. This evolution raises a distinct question: how do you craft prompts that produce solid video results rather than vague, unusable footage? This post walks through what effective prompts look like, why they matter, and how you can build them.
Why prompts matter
When you use a video generation system (text-to-video, image-to-video, etc.), the prompt you supply becomes the foundation of the output. A weak or vague prompt may lead to generic or low-quality results. A well-written prompt helps the system understand:
- what subject you want (who/what)
- what action or change you want (what is happening)
- style, tone, visual features (how it should look)
- constraints (format, duration, angle, etc)
For example, guidance documents for the tool Veo on Vertex AI describe how to break a prompt into subject + action + style + motion + camera cues.
If you skip these elements, you may get footage that fails to match your vision.
What an effective prompt includes
Here are typical components to include when constructing a prompt for AI-video generation:
- Subject/scene – Define clearly who or what is involved (“a young baker in a rustic kitchen”, “an electric car driving through a forest”).
- Action or event – What is happening? (“preparing dough”, “charging at a fast station and driving off”).
- Style / visual details – Indicate look and feel (“soft morning light”, “cinematic drone shot”, “8K resolution, shallow depth of field”).
- Camera / motion / format cues – If relevant: (“slow push-in camera”, “top-down view”, “16:9 widescreen”).
- Constraints / purpose – Format or usage context (“for Instagram Reels, 9:16 format”, “under 30 seconds”, “with on-screen subtitles”).
Here is a sample prompt combining these:
“A senior baker kneads dough in a sunlit rustic kitchen, early morning light streaming through large windows, gentle depth of field, close-up shot, 16:9 widescreen, warm colour palette, 8K resolution.”
Because the prompt is detailed, the chances of getting a usable result are higher.
Common pitfalls and how to avoid them
- Too vague prompts: E.g., “make a nice video of a car”. Without more detail (type of car, setting, action, style) the video may lack focus.
- Over-complicated or busy prompts: Including too many conflicting style instructions may confuse the model. Better to keep it clear and prioritized.
- Ignoring the format or platform: If you want a 9:16 format for social media but don’t specify it, your output may be unsuitable for your use case.
- Skipping review and iteration: The first output may not be perfect. Review it, identify what is off, tweak the prompt, and regenerate. This is normal in prompt-engineering practice.
Best practices you can adopt
- Start with a simple prompt and gradually add detail until the results improve.
- Use consistent terminology: if you specify “close-up”, stick to standard terms rather than unusual phrasing. Many tools expect common keywords.
- Consider the output constraints early (format, duration, resolution).
- Use scene progression cues if the video has multiple parts (e.g., “then the camera transitions to a wide shot”).
- After generation, evaluate: Does it match your subject, action, style, and format? If not, refine the prompt accordingly.
Final thoughts
Prompting an AI to generate video is no longer purely experimental with the right prompt you can get usable output. But it does require clarity, structure, and iteration. Treat the prompt as your blueprint: define the subject, the action, the style, the camera, and the purpose. Then review the result, refine it, and repeat until it serves your need.
