How to Write Prompts for Any AI Video Generator: The Performance Marketer's Guide

The Core Concept
Successful AI video generation isn't about guessing random phrases; it's about applying a rigid syntax of "Subject + Action + Style" to control the model's latent space. By treating prompts as code rather than creative writing, marketers can produce predictable, high-converting assets at scale.

The Strategy
Shift from manual "prompt-and-pray" tactics to programmatic creative workflows. Use specific camera terminology (pan, tilt, orbit) to direct the AI, and leverage tools that automate the technical layer so you can focus on the marketing hook.

Key Metrics

• Creative Refresh Rate: Aim for 3-5 new variants per week per product.
• Cost Per Creative: Target <$10 per usable video asset.
• Thumb-Stop Rate: Benchmark is >30% for top-performing video ads.

Tools like Koro can automate the technical heavy lifting, turning simple product URLs into optimized video scripts without manual prompting.

Prompt Engineering for Video is the systematic process of structuring text inputs to guide generative AI models in creating specific visual sequences with temporal consistency. Unlike static image prompting, video prompting specifically focuses on controlling motion, physics, and continuity over time.

Most marketers treat AI video generators like magic 8-balls—they type a vague idea and hope for a viral ad. That approach burns credits and patience. In my experience working with D2C brands, the difference between a usable ad and a hallucinated mess usually comes down to specificity. You cannot just say "make a cool shoe ad." You must define the lighting, the camera movement, and the texture of the floor.

Why Precision Matters in 2025

The market for AI video generators is exploding, with the sector projected to grow significantly by 2032 [2]. As these models get more powerful, they also get more sensitive to input nuances. A single word change—swapping "running" for "sprinting"—can completely alter the energy and pacing of your output. For e-commerce brands, this precision is money. You need the product to look premium, not like a warping cartoon.

Every effective video prompt follows a three-part structure. Memorize this syntax, and you will stop wasting credits on unusable footage. It forces the AI to prioritize the most critical visual elements first.

1. Subject (The "Who" or "What")

Define your protagonist clearly. Don't just say "a woman." Say "a professional dermatologist in a white lab coat, mid-30s, confident expression."

• Micro-Example: Instead of "dog," use "Golden Retriever puppy with a red bandana."

2. Action (The "Doing")

This is where video differs from images. You must describe the movement. Static verbs kill video generation. Use dynamic verbs that imply duration.

• Micro-Example: Instead of "standing there," use "slowly turning to face the camera while smiling."

3. Style (The "How")

This dictates the aesthetic. Is it a gritty documentary or a glossy commercial? This is where you inject technical terms like "cinematic lighting," "4k," "unreal engine 5 render," or "shot on 35mm."

• Micro-Example: Instead of "realistic," use "photorealistic, soft studio lighting, shallow depth of field, bokeh background."

Pro Tip: If you are tired of manually typing these specs for every single variant, tools like Koro automate this entire layer. You provide the product URL, and Koro's "Brand DNA" engine automatically applies the correct style cues—lighting, tone, and pacing—to match your brand voice, saving you hours of trial and error.

Camera movement is the secret language of cinema. AI models understand these terms perfectly. Using them stops the AI from making random, nauseating camera jerks and forces it to move with purpose.

Essential Camera Movements

1. Pan: Moving the camera horizontally from a fixed point.
   • Micro-Example: "Pan right to reveal the product on the table."
2. Tilt: Moving the camera vertically from a fixed point.
   • Micro-Example: "Tilt up from the shoes to the model's face."
3. Orbit: Circling around the subject.
   • Micro-Example: "Slow orbit around the perfume bottle to show reflections."
4. Zoom: Changing the focal length to get closer or further.
   • Micro-Example: "Fast zoom in on the logo for impact."

Lighting and Atmosphere

Lighting isn't just about brightness; it's about mood. "Golden hour" creates warmth and nostalgia. "Cyberpunk neon" creates energy and modernity. Be specific about the light source.

• Volumetric Lighting: Creates visible beams of light (god rays).
• Rembrandt Lighting: Classic, dramatic portrait lighting.
• High Key: Bright, evenly lit, minimal shadows (great for cosmetics).

If you are running a lean team, memorizing all these terms can be a bottleneck. This is where Koro's Competitor Ad Cloner shines. Instead of you guessing the prompt for a lighting setup, Koro analyzes a winning competitor ad, identifies the visual style, and programmatically recreates that "look" for your product automatically.

Stop treating AI video as a fun experiment and start treating it as a production pipeline. Here is the exact 30-day roadmap I recommend to D2C brands looking to operationalize this tech.

The Goal: By day 30, you should not be writing prompts from scratch. You should be reviewing outputs generated by your system.

The Challenge: Bloom Beauty, a scaling cosmetics brand, hit a wall. Their competitor launched a viral "Texture Shot" ad that was crushing it. Bloom's creative team tried to replicate the style manually but couldn't get the lighting and viscosity right without an expensive studio shoot. They were stuck.

The Solution: They turned to Koro's Competitor Ad Cloner. Instead of renting a studio, they fed the competitor's ad into Koro. The AI analyzed the structural elements—the macro zoom, the slow-motion pour, the high-key lighting—and cloned the structure of the ad. Crucially, Koro applied Bloom's specific "Scientific-Glam" Brand DNA to the output, ensuring it didn't look like a cheap rip-off.

The Results:

• 3.1% CTR: The AI-generated variant became their top performer.
• 45% Lift: It beat their own manual control ad by nearly half.
• Speed: They went from "idea" to "live ad" in under 2 hours.

This illustrates the power of programmatic creative. Bloom didn't need a prompt engineer to figure out the exact diffusion model settings for "viscous liquid pour." They just needed a reference and a tool smart enough to translate it.

There are two ways to handle AI video: the craftsman approach (manual) and the industrial approach (automated). For hobbyists, manual is fine. For performance marketers, it's a trap.

The Verdict: Manual prompting is great for specific, high-art brand films. But for the day-to-day trenches of Facebook and TikTok ads, where creative fatigue sets in after 4 days, you need volume. Koro excels at rapid UGC-style ad generation at scale, but for cinematic brand films with complex VFX, a traditional studio or high-end manual prompting is still the better choice.

Don't get distracted by vanity metrics like "views." In e-commerce, video has one job: sales. Here are the KPIs you must track to validate your prompt engineering strategy.

1. Thumb-Stop Rate (3-Second View Rate):
   • Benchmark: Aim for >30%.
   • Action: If this is low, your Action prompt is weak. The opening movement isn't grabbing attention. Try using "fast zoom" or "sudden transition" prompts.
2. Hold Rate (15-Second View Rate):
   • Benchmark: Aim for >10%.
   • Action: If viewers drop off, your Style or narrative is boring. Use prompts that change the angle or setting every 3 seconds to reset attention.
3. Creative Refresh Rate:
   • Benchmark: 3-5 new ads per week.
   • Action: If you can't hit this, your workflow is broken. This is the primary signal to switch from manual prompting to an automated solution like Koro.

Around 60% of marketers now use AI tools in some capacity [1], but few are measuring the efficiency of their creative pipeline. The winners in 2025 will be the ones who treat creative production as a measured science.