The Complete Guide to AI Video Prompt Engineering in 2025

Prompt engineering for AI video is like perfecting a recipe: the same ingredients yield wildly different results depending on technique. After spending countless hours generating videos across every major platform, I've distilled what actually works into a practical framework. Let's cut through the noise and focus on techniques that produce consistent, professional results.

Why Video Prompts Are Different

If you've worked with image generators like Midjourney or DALL-E, you might think video prompts work the same way. They don't. Video adds a temporal dimension—movement, pacing, transitions—that transforms prompt engineering from a single instruction into orchestrating a sequence.

Think of it like the difference between taking a photograph and directing a scene. For a photo, you set up the shot. For video, you need to choreograph what happens over time:

• How does the camera move?
• What actions unfold?
• How long does each element last?
• What's the emotional arc?

These questions require vocabulary and structure that go beyond static image prompts.

The Six-Layer Framework

Professional video prompts follow a structured approach. I call it the six-layer framework—each layer adds specificity that guides the AI toward your vision:

Layer 1: Subject and Action

Define your focus with precision. Vague subjects produce vague results.

Weak: "A woman in a garden" Strong: "A woman in a flowing red dress walking slowly through rose bushes, gently touching petals as she passes"

The strong version specifies clothing, movement speed, and interaction with the environment. Every detail constrains the AI's interpretation toward your intent.

Layer 2: Shot Type and Framing

Cinematographers have spent a century developing visual grammar. Use it.

Example: "Medium tracking shot, camera positioned at waist height, following from the side"

Layer 3: Camera Movement

Static shots feel amateurish. Movement creates energy and guides attention.

Example: "Slow dolly forward through the doorway, maintaining eye-level perspective"

Layer 4: Lighting and Atmosphere

Lighting sets mood more powerfully than any other element.

Example: "Golden hour lighting with volumetric rays filtering through dusty windows, warm color grade"

Layer 5: Technical Specifications

Name specific technical parameters when you want precise control:

• Lens: 35mm (natural), 50mm (portrait), 85mm (compression), 24mm (wide)
• Depth of field: Shallow (bokeh background) vs. deep (everything sharp)
• Frame rate: 24fps (cinematic), 60fps (smooth), 120fps (slow motion)
• Aspect ratio: 16:9 (standard), 2.39:1 (cinematic), 9:16 (vertical)

Example: "Shot on 85mm lens, shallow depth of field with creamy bokeh, slight film grain"

Layer 6: Duration and Pacing

Video unfolds over time. Specify rhythm:

• Scene duration (3-10 seconds typical)
• Transition style (cut, dissolve, wipe)
• Pacing (slow/contemplative vs. fast/energetic)
• Beat timing for music synchronization

Example: "6-second shot with slow, deliberate movement, holding on the final frame for 1 second"

Putting It Together: Full Prompt Examples

Here's how layers combine into professional prompts:

Cinematic Portrait:

Medium close-up of a weathered fisherman's face, early morning blue hour,
shot on 85mm lens with shallow depth of field. Gentle handheld micro-movements,
soft rim lighting from behind creating a halo effect on his gray hair.
Contemplative expression, eyes looking slightly off-camera.
Cool color grade with lifted shadows, 5 seconds duration.

Action Sequence:

Wide tracking shot following a parkour athlete running across urban rooftops
at sunset. Dynamic steadicam movement maintaining consistent distance,
golden hour backlighting creating dramatic silhouette. 24fps cinematic motion,
slight slow-motion at 0.8x speed. High contrast, teal-orange color grade.
8 seconds with building intensity.

Product Showcase:

Slow 360-degree orbit around a luxury watch on black velvet surface.
Macro lens capturing intricate dial details, controlled studio lighting
with soft key light and subtle fill. Shallow depth of field isolating
the subject, gentle reflections on crystal. Premium feel with
slow, deliberate camera movement. 10 seconds duration.

Negative Prompting: Telling AI What to Avoid

Equally important is specifying what you don't want. Each platform handles this differently:

Common negative prompts:

• Blurry footage, motion blur artifacts
• Distorted faces, anatomical errors
• Watermarks, text overlays
• Unnatural movements, jerky transitions
• Low resolution, compression artifacts

Platform-specific syntax:

Example: "Avoid: blurry footage, distorted facial features, watermarks, jerky camera movement, oversaturated colors"

Style Reference Stacking

Want a distinctive aesthetic? Combine 2-3 film references:

Formula: [Film A] color grading + [Film B] atmosphere + [Film C] camera movement

Examples:

• "Blade Runner 2049 color grading plus Se7en atmosphere plus Heat camera movement"
• "Wes Anderson symmetry plus Studio Ghibli color palette plus Terrence Malick natural lighting"
• "Mad Max: Fury Road energy plus Roger Deakins lighting plus Spielberg blocking"

Limit to 3 references. More creates conflicting signals.

Platform-Specific Optimization

Each model has strengths. Match your prompt style to the platform:

Veo 3 JSON Example:

{
  "subject": "woman in red dress",
  "action": "walking through garden",
  "shot_type": "medium tracking",
  "camera_movement": "dolly right to left",
  "lighting": "golden hour, volumetric",
  "lens": "35mm",
  "duration": "6 seconds"
}

The 5-10-1 Cost Optimization Rule

Premium renders are expensive. Use this workflow:

1. 5 variations on lower-cost models (40-60 credits each)
2. 10 iterations refining the best candidate
3. 1 final render on premium tier (~350 credits)

This reduces costs from thousands to around 1,000 credits while maintaining quality.

Common Mistakes to Avoid

After reviewing hundreds of prompts, these errors appear most often:

Building Your Prompt Library

Create templates for common scenarios:

Interview Setup:

Medium shot, subject positioned rule-of-thirds left, eye-level camera,
[LIGHTING_SETUP], shallow depth of field blurring background,
subtle handheld micro-movements for natural feel, [DURATION].

B-Roll Nature:

[SHOT_TYPE] of [SUBJECT], [TIME_OF_DAY] lighting,
slow [CAMERA_MOVEMENT], [LENS]mm lens, deep focus,
[COLOR_GRADE] palette, [DURATION].

Product Hero:

[ORBIT_DIRECTION] orbit around [PRODUCT] on [SURFACE],
studio lighting with [KEY_LIGHT_POSITION] key and subtle fill,
macro detail moments, [LENS]mm, pristine reflections, [DURATION].

Fill in brackets for specific needs. Build a library organized by use case.

Iteration Strategy

Perfect prompts emerge through systematic refinement:

1. Start simple: Core subject and action only
2. Add one element: Test single additions
3. Document what works: Keep a log of effective phrases
4. A/B test phrasing: Same concept, different words
5. Save winners: Build your prompt library

Log format:

Prompt: [full prompt]
Model: [platform used]
Result: [1-5 rating]
Notes: [what worked/didn't]

Quality Review Checklist

Before finalizing any AI video, verify:

• Subject consistency throughout
• Natural motion (no jerkiness)
• Lighting continuity
• No facial distortions
• Color grade consistency
• Appropriate pacing
• Clean audio (if applicable)
• No watermarks or artifacts

Next Steps

Prompt engineering improves with practice. Start with simpler shots, master each layer, then combine them. The goal isn't memorizing terminology—it's developing intuition for what makes video compelling.

Keep a generation log. Review what worked. Build your library. The difference between amateur and professional AI video often comes down to prompt precision.

Your camera is waiting. Start filming.