If you write code for a living, you already know how to get good results from an AI video model. You just haven't noticed yet. The first time I tried text-to-video, I typed something like "a cool product shot of a coffee mug" and hit generate. The result was fine in the way a function that returns undefined is "fine": it ran, but it didn't do what I meant. I tried again with more adjectives, and the result got worse.
Things only improved once I started treating each prompt like a tiny spec: clear inputs, explicit constraints, one variable changed per run. This post is the checklist I ended up with. I used flow ai, a browser-based AI video workspace, for all of these tests, but the approach should carry over to most modern video models.
Aspect ratio: 16:9 for a landing page or YouTube, 9:16 for Shorts, Reels, or TikTok.
Length: short clips are cheaper to iterate on. I start at 4 seconds and only go longer once the shot works.
Resolution: 720p is enough for drafts. Save 1080p for the version you actually ship. This is the "return type" of your prompt. If you pick it last, you end up cropping a widescreen clip into a vertical one and losing the subject.
SUBJECT: a matte black ceramic coffee mug with steam rising
ACTION: slowly rotates 90 degrees on a wooden table
SETTING: morning kitchen, soft window light from the left
CAMERA: static close-up, shallow depth of field
I don't literally send the labels (though sometimes it helps), but I write the sentence in that order:
A matte black ceramic coffee mug with steam rising slowly rotates 90 degrees on a wooden table in a morning kitchen, soft window light from the left. Static close-up, shallow depth of field.
Each field answers one question. When a generation goes wrong, I can usually point to the field that was vague, the same way you'd point to the parameter that caused a bug.
My loop looks like this:
Run the baseline prompt.
Change one field (for example, CAMERA: slow push-in instead of static). Compare the two clips side by side.
Keep the better one as the new baseline.
It feels slow, but it converges much faster than rewriting the whole prompt each time.
For those cases, image-to-video works better. You upload the frame you already signed off on and describe only the motion. The model doesn't have to guess what the mug looks like; it only has to animate it. In flow ai you can also keep reference images and clips next to the prompt, which helps when a shot needs a firmer visual anchor than words can give. A rule of thumb I use:
Goal Input
Explore ideas, moods, openings Text only
Keep a real product or character consistent Image + motion prompt
Match a specific look or style Text + reference images/clips
The flow ai generator lists several models in one menu, including Veo 3.1 Fast, Veo 3.1, Seedance 2.0, Seedance 2.5, Kling 3, MiniMax H3, and a few others. My workflow:
Fast model for drafts. Veo 3.1 Fast at 720p and 4 seconds is the default, and it's cheap enough to run many variations while the prompt is still changing.
Stronger model for the final pass. Once the prompt is stable, I rerun it on a heavier model and compare.
Same prompt, different models. Running an identical spec on two models is a quick way to see which one handles your type of motion better.
Having all of them behind one credit balance means I'm not juggling five accounts just to run an A/B test.
Draft at low resolution and short length. Scale up only the winner.
Don't "regenerate and hope." If a result is wrong, fix the spec first.
Keep a small text file of prompts that worked. It's your snippet library.
flow ai also has a daily check-in that adds bonus credits over a seven-day streak, which is handy if you're experimenting a little every day rather than in one big batch.
Product motion tests before paying for a reshoot.
Openings and hooks: testing the first two seconds of an ad or a demo video.
Moving mood boards: showing a client or teammate two or three directions instead of a static slide.
Treat the output as a draft you can review, not a final asset you have to defend.
TL;DR
Decide aspect ratio, length, and resolution before you write the scene.
Write prompts as subject → action → setting → camera.
Change one variable per run and keep the best result as your baseline.
Use image-to-video when consistency matters. Draft on a fast model, finish on a stronger one.
Treat credits like CI minutes.
If you've been getting random results from video models, try writing your next prompt like a spec and see how much more predictable it gets. I'd love to hear what prompt structures work for you in the comments.