AI video generation has become incredibly powerful, but many creators find themselves frustrated when their carefully crafted prompts produce unexpected results. Whether you're using Sora, Runway ML, Luma Dream Machine, or Kling AI, understanding why these models sometimes ignore your instructions—and how to fix it—can dramatically improve your success rate.
The Root of the Problem: How AI Video Models Actually Work
AI video models don't "understand" your prompts the way humans do. Instead, they rely on pattern recognition from massive datasets of video-text pairs. When you write "a person walking in the rain," the model searches for similar patterns it learned during training and attempts to recreate them.
This fundamental limitation leads to several key issues:
1. Frame-by-Frame Generation Without Memory
Most AI video models generate each frame individually or in small chunks, without maintaining a consistent "mental model" of the scene. This is why you might see a character's appearance subtly shift throughout a video, or objects mysteriously appear and disappear.
2. Physics Confusion
Recent research shows that AI video models can mimic physics but don't truly understand it. They learn patterns like "balls fall down" from training data, but fail when encountering novel scenarios not represented in their training. This is why you might see water flowing upward or objects floating unnaturally.
3. Prompt Interpretation Hierarchy
When processing your prompt, AI models follow an internal hierarchy of importance. Studies reveal the typical priority order is:
- Color (highest priority)
- Size
- Velocity
- Shape (lowest priority)
This explains why changing a character's clothing color might work perfectly, but altering their physical proportions proves challenging.
Common Prompt Errors and How to Fix Them
Error #1: Vague or Ambiguous Descriptions
Problematic Prompt:
"Nice scene with animals"
Why it fails: The model doesn't know what "nice" means or which animals to include.
Fixed Prompt:
"Medium shot of a golden retriever playing with a red ball in a sunny park, gentle breeze moving the grass"
Key improvement: Specific subjects, actions, settings, and atmospheric details.
Error #2: Contradictory Instructions
Problematic Prompt:
"Dark noir atmosphere with bright sunny colors and fast slow-motion"
Why it fails: The model receives conflicting signals and averages them out, creating muddy results.
Fixed Prompt:
"Film noir atmosphere: low-key lighting, desaturated palette, slow dolly-in on character"
Key improvement: One clear visual direction per prompt.
Error #3: Negative Phrasing
Problematic Prompt:
"A beach scene with no people, don't show any buildings"
Why it fails: AI models struggle with negative instructions and often generate the forbidden elements anyway.
Fixed Prompt:
"Empty sandy beach at sunset, gentle waves, seashells scattered on shore"
Key improvement: Describe what you want to see, not what you want to avoid.
Error #4: Overcomplicated Scenarios
Problematic Prompt:
"Five people dancing while juggling while it's raining while cars race in the background while the camera spins 360 degrees"
Why it fails: Too many simultaneous actions overwhelm the model's processing capacity.
Fixed Prompt:
"Wide shot of street dancers performing in light rain, camera slowly pans left"
Key improvement: Focus on one primary action with simple camera movement.
Error #5: Missing Technical Specifications
Problematic Prompt:
"Person walking"
Why it fails: Lacks crucial details about framing, movement, and style.
Fixed Prompt:
"Medium shot tracking the subject, young woman in business attire walking confidently down modern office hallway, natural lighting, 35mm lens feel"
Key improvement: Include camera angle, lighting, and cinematic terminology.
Platform-Specific Solutions
Sora (OpenAI)
- Keep prompts under 400 characters
- Focus only on visual elements (no audio descriptions)
- Avoid text overlay requests
- Use cinematic terminology like "establishing shot" or "close-up"
Runway ML Gen-3/4
- Include specific camera movements ("slow dolly forward")
- Specify lighting conditions ("soft natural light")
- Add composition details ("rule of thirds framing")
- Use film production terminology
Luma Dream Machine
- Place camera type first in your prompt structure
- Be explicit about duration if you need longer clips
- Include environmental context and background details
- Use their "Enhanced Prompt" feature for beginners
Kling AI
- Focus on realistic physics (avoid impossible movements)
- Describe motion patterns naturally
- Keep interactions simple (one physics action at a time)
- Avoid contradictory motion instructions
Advanced Prompting Strategies
The Specificity Pattern
[Detailed subject] [specific action] in [detailed environment]. [Lighting description]. [Camera instruction]. [Style reference].
Example:
Professional chef in white uniform dicing vegetables with precise knife movements in modern stainless steel kitchen. Warm pendant lighting from above. Static camera at eye level. Documentary style cinematography.
The Priority Pattern
Most important: [critical element]. [Secondary elements]. Camera [movement] to follow [subject]. Style: [specific aesthetic].
Example:
Most important: vintage red motorcycle. Rider in leather jacket starts engine. Camera slowly circles vehicle. Style: 1970s film grain aesthetic.
The Physics Pattern
[Subject] [action] with realistic physics. [Environmental elements] respond naturally to [forces].
Example:
Basketball bounces on wooden court with realistic physics. Court dust particles rise naturally from impact. Net sways gently after ball passes through.
Troubleshooting Generation Failures
"Can't Generate Your Video" Errors
Common causes:
- Content policy violations
- Overly complex prompts
- Server overload during peak hours
- Technical specification conflicts
Quick fixes:
- Simplify your prompt
- Remove any potentially flagged content
- Try generating during off-peak hours
- Check platform-specific duration limits
When to Pivot Your Approach
If you've tried multiple prompt variations without success:
- Switch input methods: Try image-to-video instead of text-to-video
- Change models: Different platforms excel at different content types
- Break complex concepts into simpler components
- Consider hybrid approaches: Use AI for some elements, traditional editing for others
Quality Control Checklist
Before submitting your prompt, ask yourself:
- Is my main subject clearly defined?
- Do I specify the camera angle and movement?
- Are my instructions positive (what to show) rather than negative?
- Is the lighting/atmosphere described?
- Are there any contradictory elements?
- Does it follow platform-specific guidelines?
- Is the complexity appropriate for a 5-20 second clip?
The Future of AI Video Prompting
As AI video models evolve, we're seeing improvements in prompt adherence and physics understanding. Google's Veo 3 shows early signs of visual reasoning, while newer models are beginning to maintain better temporal consistency.
However, the fundamental challenge remains: these models excel at pattern matching but struggle with true understanding. The key to success lies in working with their strengths while compensating for their limitations through precise, structured prompting.
Remember, every failed generation is a learning opportunity. Document what works, understand each platform's quirks, and continuously refine your approach. With practice and the right techniques, you can achieve consistent, high-quality results that bring your creative vision to life.
The era of AI video generation is just beginning, and mastering these prompting fundamentals will give you a significant advantage as the technology continues to evolve.
