Avoid the "slot machine" approach of direct text-to-video. Instead, use image generation tools that offer multiple variations for each prompt. This allows you to conversationally refine scenes, select the best camera angles, and build out a shot sequence before moving to the animation phase.
Advanced generative media workflows are not simple text-to-video prompts. Top customers chain an average of 14 different models for tasks like image generation, upscaling, and image-to-video transitions. This multi-model complexity is a key reason developers prefer open-source for its granular control over each step.
Successful AI video production doesn't jump from text to video. The optimal process involves scripting, using ChatGPT for a shot list, generating still images for each shot with tools like Rev, animating those images with models like VEO3, and finally, editing them together.
AI tools rarely produce perfect results initially. The user's critical role is to serve as a creative director, not just an operator. This means iteratively refining prompts, demanding better scripts, and correcting logical flaws in the output to avoid generic, low-quality content.
Most generative AI tools get users 80% of the way to their goal, but refining the final 20% is difficult without starting over. The key innovation of tools like AI video animator Waffer is allowing iterative, precise edits via text commands (e.g., "zoom in at 1.5 seconds"). This level of control is the next major step for creative AI tools.
Integrate external media tools, like an Unsplash MCP for Claude, into your data generation prompts. This programmatically fetches real, high-quality images for your prototypes, eliminating the manual work of finding photos and avoiding the broken links or irrelevant images that LLMs often hallucinate.
Achieve higher-quality results by using an AI to first generate an outline or plan. Then, refine that plan with follow-up prompts before asking for the final execution. This course-corrects early and avoids wasted time on flawed one-shot outputs, ultimately saving time.
To get superior results from image generators like Midjourney, structure prompts around three core elements: the subject (what it is), the setting (where it is, including lighting), and the style. Defining style with technical photographic terms yields better outcomes than using simple adjectives.
Exceptional AI content comes not from mastering one tool, but from orchestrating a workflow of specialized models for research, image generation, voice synthesis, and video creation. AI agent platforms automate this complex process, yielding results far beyond what a single tool can achieve.
Leverage AI as an idea generator rather than a final execution tool. By prompting for multiple "vastly different" options—like hover effects—you can review a range of possibilities, select a promising direction, and then iterate, effectively using AI to explore your own taste.
When analyzing video, new generative models can create entirely new images that illustrate a described scene, rather than just pulling a direct screenshot. This allows AI to generate its own 'B-roll' or conceptual art that captures the essence of the source material.