The Secret to Better AI Videos: Stop Writing Prompts

Your ideas matter more than your prompts! This tutorial will show you how to use the documentation from a variety of AI video models to be able to write better prompts for text-to-video generators. Pour your creative energy into your ideas and storytelling rather than crafting a prompt. In this tutorial, I show how to use DeepSeek, ChatGPT, and Google Gemini to write better AI video prompts using documentation from Runway, Veo, Hailuo, Kling, Luma Dream Machine, and Vidu. This workflow keeps me focused on what matters most — the idea, the perspective, the story.

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If you're wondering... the AI videos of myself were all made with Vidu. I love their character reference:
My Hotel Reverie: https://youtu.be/tooqL1uxMfU
Tutorial on using Vidu's character reference: https://youtu.be/apeg-0DKsaQ

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???? CHAPTERS:
00:00 Intro: The Art is Your Idea
01:11 Getting prompt documentation from Hailuo, Kling, Luma, Runway, Veo, Vidu
02:07 Creating a custom GPT for writing better AI video prompts
02:41 Example of iterating with a Prompt Sidekick
04:49 How I use my Prompt Sidekick
07:13 Is Prompt Engineering Still Even a Thing?
07:49 Conclusion

☕️ Prompts from coffee shop example:
1️⃣ Kling: Subject: An old man in his late 70s, gray hair combed back, corduroy jacket slightly worn. Subject Movement: Seated, gazing into a chipped coffee mug. As the waitress approaches, he lifts his eyes.Scene: A small-town diner with faded wallpaper, old wood trim, and a faint scent of coffee and rain. Scene Description: The waitress arrives quietly, stops beside him. Camera Language: [Low angle on man], then [tilt up] to reveal her mid-pour, framed gently in warm light. Slight lens blur on background. Lighting: Soft overhead lamp glow, golden hour tones bleeding into deep shadows. Atmosphere: Intimate, rustic, human — a captured moment from a quiet story, as if remembered.
2️⃣ Veo: Low angle composition of a small-town diner booth: the old man sits still, framed by checkered tiles and an old coat rack behind him. His mug is empty, his eyes distant. Warm overhead lighting softens the wrinkles on his face. The waitress walks into frame, stopping beside him. The camera tilts up, catching her mid-pour. She looks down gently. A soft smile lingers between them. The lighting is natural, no harsh contrast, with ambient shadows and diffused warmth. Sound is minimal — maybe just a ceiling fan and distant kitchen clatter. Scene evokes quiet companionship, slow cinema pacing, and deep emotional texture.
3️⃣ Hailuo: A small-town diner late at night, the mood quiet and timeless. An elderly man with a weathered face but soulful eyes sits in a red vinyl booth, his empty mug resting on the table in front of him. Medium close-up framing. The focus is on the table and his weary face just above it. A warm ceiling light glows softly, shadows stretching gently across the diner walls, a window shows the empty street outside. The waitress steps into frame from the side, holding a glass coffee pot. She stops at the booth, pouring into his mug. He looks up, meeting her gaze with a faint smile. The hum of the diner fills the silence. Grounded, cinematic intimacy with restrained camera movement.
4️⃣ Vidu: Cinematic low-angle shot captures an old man with a weathered face but soulful eyes sitting at a corner booth with a white ceramic mug in front of him, in a quiet small town american diner. The waitress enters frame with a coffee pot carafe and pours coffee slowly into his mug. Their eye contact is brief but tender. The window shows faint reflections of streetlamps outside. cinematic calm, shallow depth of field.
5️⃣ Runway: Cinematic low-angle shot captures an old man with a weathered face but soulful eyes sitting at a corner booth with a white ceramic mug in front of him, in a quiet small town american diner. The waitress enters frame with a coffee pot carafe and pours coffee slowly into his mug. Their eye contact is brief but tender. The window shows faint reflections of streetlamps outside. cinematic calm, shallow depth of field.

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