Technical Guide: AI Video Prompt Cookbook for Consistent Generation

Practical Summary and Purpose

The AI Video Prompt Cookbook provides a standardized framework for creators, marketers, and small teams to generate usable AI video clips through image-to-video and text-to-video workflows. Rather than focusing on one-off demo prompts, the tool emphasizes repeatable testing, source image preparation, and strict motion constraints. It is designed for users who need to produce clips that are suitable for professional editing, such as social media hooks or product advertisements, where visual consistency and editability are paramount. By utilizing a structured prompt card format, teams can ensure that their video outputs remain aligned with brand requirements and project goals.

Prerequisites and Target Audience

This guide is intended for product marketers, social media creators, and brand teams who require a systematic way to compare AI video outputs. It is not an API integration guide; instead, it serves those who evaluate AI video performance visually. Users should have access to source images that serve as creative anchors for their projects. The methodology assumes that the user is capable of performing iterative testing and evaluating results based on specific criteria such as subject fidelity and motion usefulness. The tool is best suited for those who need to produce content that can be cropped, captioned, and reused in larger video sequences.

The Prompt Card Format

To maintain consistency across tests, the cookbook mandates the use of a specific prompt card format. This structure ensures that the idea, constraints, and results remain connected throughout the production process. The two most critical fields within this format are Subject to preserve and Motion. Many users make the mistake of simply describing the image again, but effective prompts must explicitly define what should move and what should remain static. By documenting these details, creators can isolate variables and understand why a specific generation succeeded or failed.

Clip job:
Source image:
Subject to preserve:
Motion:
Camera:
Framing:
Style:
Negative constraints:
Success criteria:
Result notes:
Next change:

Documented Workflow for Product Ads

When working with a studio product image, the workflow focuses on maintaining the integrity of the product while introducing subtle motion. The goal is to create a 5-second vertical ad where the product shape, label, and color remain unchanged. The prompt should specify camera movements like a slow push-in and define the framing to allow for caption space. By setting negative constraints—such as prohibiting background replacement or label distortion—creators can ensure the output is usable for ecommerce. This structured approach prevents the AI from hallucinating unwanted elements or altering the product's appearance during the generation process.

UGC-Style Hook Generation

For social media content that requires a creator-style aesthetic, the cookbook suggests a different approach. The prompt should focus on maintaining the stability of the product and the hand position while introducing natural, handheld-style movement. The camera instructions should emphasize a close vertical shot with mild energy rather than dramatic zooms. This helps the output feel authentic and casual, which is essential for UGC-style hooks. By keeping the product centered and leaving room for lower-third captions, the resulting clip remains highly functional for social platforms without requiring extensive post-production fixes.

Model Comparison and Evaluation

To compare different AI models effectively, the cookbook advises keeping the job and the prompt stable. Changing the prompt after viewing an initial result turns the process into a prompt rewrite test rather than a model comparison. The evaluation should be based on a scorecard that weights criteria such as subject fidelity (30%), motion usefulness (20%), prompt adherence (20%), editability (20%), and retry cost (10%). This quantitative approach prevents creators from keeping clips that look impressive but fail to meet the specific requirements of the original clip job.

Managing Failures and Iteration

The cookbook provides a clear strategy for addressing common failures such as product drift, background takeover, and text distortion. If a product's shape or label changes, the user should add stricter preservation constraints and simplify the source image. If the background moves too aggressively, the prompt should be adjusted to request subtle environmental motion. For issues with unreadable text, the best practice is to avoid generating text within the AI tool entirely and instead add it during the editing phase. These failure notes serve as a guide for refining prompts in subsequent iterations.

Limitations and Implementation

It is important to note that this cookbook is not a substitute for professional editing workflows; it is a tool for generating raw assets. Users should be aware that AI video generation can be unpredictable, and the goal is to minimize the retry cost through better prompting. The suggested folder structure, including directories for product-video-prompts, ugc-ad-prompts, and failure-notes, helps maintain organization as the project scales. For further information on image-to-video workflows, users can refer to the resource at https://lumiying.com/tools/image-to-video. The cookbook is licensed under MIT, allowing for adaptation to specific brand and safety requirements.