everything I learned after 10,000 AI video generations (the complete guide)
https://www.reddit.com/r/PromptEngineering/comments/1mvfcrr/everything_i_learned_after_10000_ai_video

General Discussion
this is going to be the longest post I’ve written but after 10 months of daily AI video creation, these are the insights that actually matter…

I started with zero video experience and $1000 in generation credits. Made every mistake possible. Burned through money, created garbage content, got frustrated with inconsistent results.

Now I’m generating consistently viral content and making money from AI video. Here’s everything that actually works.

The fundamental mindset shifts:
1. Volume beats perfection
Stop trying to create the perfect video. Generate 10 decent videos and select the best one. This approach consistently outperforms perfectionist single-shot attempts.

2. Systematic beats creative
Proven formulas + small variations outperform completely original concepts every time. Study what works, then execute it better.

3. Embrace the AI aesthetic
Stop fighting what AI looks like. Beautiful impossibility engages more than uncanny valley realism. Lean into what only AI can create.

The technical foundation that changed everything:
The 6-part prompt structure:
[SHOT TYPE] + [SUBJECT] + [ACTION] + [STYLE] + [CAMERA MOVEMENT] + [AUDIO CUES]
This baseline works across thousands of generations. Everything else is variation on this foundation.

Front-load important elements
Veo3 weights early words more heavily. “Beautiful woman dancing” ≠ “Woman, beautiful, dancing.” Order matters significantly.

One action per prompt rule
Multiple actions create AI confusion. “Walking while talking while eating” = chaos. Keep it simple for consistent results.

The cost optimization breakthrough:
Google’s direct pricing kills experimentation:

$0.50/second = $30/minute

Factor in failed generations = $100+ per usable video

Found companies reselling veo3 credits cheaper. I’ve been using these guys who offer 60-70% below Google’s rates. Makes volume testing actually viable.

Audio cues are incredibly powerful:
Most creators completely ignore audio elements in prompts. Huge mistake.

Instead of: Person walking through forestTry: Person walking through forest, Audio: leaves crunching underfoot, distant bird calls, gentle wind through branches

The difference in engagement is dramatic. Audio context makes AI video feel real even when visually it’s obviously AI.

Systematic seed approach:
Random seeds = random results.

My workflow:

Test same prompt with seeds 1000-1010

Judge on shape, readability, technical quality

Use best seed as foundation for variations

Build seed library organized by content type

Camera movements that consistently work:
Slow push/pull: Most reliable, professional feel

Orbit around subject: Great for products and reveals

Handheld follow: Adds energy without chaos

Static with subject movement: Often highest quality

Avoid: Complex combinations (“pan while zooming during dolly”). One movement type per generation.

Style references that actually deliver:
Camera specs: “Shot on Arri Alexa,” “Shot on iPhone 15 Pro”

Director styles: “Wes Anderson style,” “David Fincher style” Movie cinematography: “Blade Runner 2049 cinematography”

Color grades: “Teal and orange grade,” “Golden hour grade”

Avoid: Vague terms like “cinematic,” “high quality,” “professional”

Negative prompts as quality control:
Treat them like EQ filters - always on, preventing problems:

--no watermark --no warped face --no floating limbs --no text artifacts --no distorted hands --no blurry edges
Prevents 90% of common AI generation failures.

Platform-specific optimization:
Don’t reformat one video for all platforms. Create platform-specific versions:

TikTok: 15-30 seconds, high energy, obvious AI aesthetic works

Instagram: Smooth transitions, aesthetic perfection, story-driven YouTube Shorts: 30-60 seconds, educational framing, longer hooks

Same content, different optimization = dramatically better performance.

The reverse-engineering technique:
JSON prompting isn’t great for direct creation, but it’s amazing for copying successful content:

Find viral AI video

Ask ChatGPT: “Return prompt for this in JSON format with maximum fields”

Get surgically precise breakdown of what makes it work

Create variations by tweaking individual parameters

Content strategy insights:
Beautiful absurdity > fake realism

Specific references > vague creativityProven patterns + small twists > completely original conceptsSystematic testing > hoping for luck

The workflow that generates profit:
Monday: Analyze performance, plan 10-15 concepts

Tuesday-Wednesday: Batch generate 3-5 variations each Thursday: Select best, create platform versions

Friday: Finalize and schedule for optimal posting times

Advanced techniques:
First frame obsession:
Generate 10 variations focusing only on getting perfect first frame. First frame quality determines entire video outcome.

Batch processing:
Create multiple concepts simultaneously. Selection from volume outperforms perfection from single shots.

Content multiplication:
One good generation becomes TikTok version + Instagram version + YouTube version + potential series content.

The psychological elements:
3-second emotionally absurd hook
First 3 seconds determine virality. Create immediate emotional response (positive or negative doesn’t matter).

Generate immediate questions
“Wait, how did they…?” Objective isn’t making AI look real - it’s creating original impossibility.

Common mistakes that kill results:
Perfectionist single-shot approach

Fighting the AI aesthetic instead of embracing it

Vague prompting instead of specific technical direction

Ignoring audio elements completely

Random generation instead of systematic testing

One-size-fits-all platform approach

The business model shift:
From expensive hobby to profitable skill:

Track what works with spreadsheets

Build libraries of successful formulas

Create systematic workflows

Optimize for consistent output over occasional perfection

The bigger insight:
AI video is about iteration and selection, not divine inspiration. Build systems that consistently produce good content, then scale what works.

Most creators are optimizing for the wrong things. They want perfect prompts that work every time. Smart creators build workflows that turn volume + selection into consistent quality.

Where AI video is heading:
Cheaper access through third parties makes experimentation viable

Better tools for systematic testing and workflow optimization

Platform-native AI content instead of trying to hide AI origins

Educational content about AI techniques performs exceptionally well

Started this journey 10 months ago thinking I needed to be creative. Turns out I needed to be systematic.

The creators making money aren’t the most artistic - they’re the most systematic.

These insights took me 10,000+ generations and hundreds of hours to learn. Hope sharing them saves you the same learning curve.

what’s been your biggest breakthrough with AI video generation? curious what patterns others are discovering