Common Prompt Mistakes in AI Video Generation and How to Fix Them

The gap between a mediocre AI-generated video and a good one usually comes down to the prompt. Video generation models are highly sensitive to how you describe the scene, and common mistakes in prompt structure produce predictable failure modes: motion that doesn't match the subject, awkward camera behavior, inconsistent quality across the clip, or outputs that technically fulfill the prompt but miss the intended result.

This guide covers the most common prompting mistakes, why they cause the specific problems they do, and how to fix them — with examples drawn from LTX-2.3's behavior and prompting documentation.

Mistake 1: Treating Video Prompts Like Image Prompts

The Problem

Image generation models reward rich noun phrases describing visual aesthetics: lighting, color grading, depth of field. Video models need to know what happens over time. A prompt optimized for image generation will produce static-feeling video with minimal motion or motion that seems arbitrary rather than purposeful.

The Fix

Add temporal structure to your prompts. Describe the arc of the clip explicitly: what the scene looks like at the start, how it transitions, and what state it ends in. Use progressive language: "begins with," "then," "as the camera moves."

The LTX-2.3 prompting documentation recommends writing prompts in the same order events should appear on screen. If you want a character to walk into frame and then sit down, describe those events in that order.

Example

Weak: "A woman in a red dress, cinematic lighting, high detail"

Stronger: "A woman in a red dress walks into a sunlit kitchen from the left, moves to the counter, and begins preparing coffee. The camera follows her movement from behind at a slight distance."

Mistake 2: Underspecifying Motion

The Problem

Video generation models infer motion from context when you don't describe it explicitly. Without specific motion guidance, the model defaults to the most statistically common motion for the described scene, which is often subtle camera drift or generic subject movement that doesn't match the intended creative direction.

The Fix

Describe subject motion and camera motion separately. Subject motion: what is the subject doing, how fast, in what direction. Camera motion: what is the camera doing, whether it's moving, and how.

LTX-2.3 is trained to generate temporally coherent video that matches detailed motion descriptions. The model responds to cinematographic camera descriptors: dolly in, pan left, tracking shot, static camera, slow push-in. When both subject and camera motion are specified, the output is more directed.

Example

Underspecified: "A dog running in a field"

Specified: "A golden retriever sprints across an open field, the camera tracking alongside it from a low angle. Motion is fast and fluid. As the dog reaches a tree, it slows and looks back."

Mistake 3: Including Conflicting Descriptions

The Problem

Prompts that contain contradictory elements cause the model to average competing signals rather than commit to either. Common conflicts: describing both fast and slow motion, specifying two incompatible camera behaviors, or describing visual properties that don't coexist in training data.

The Fix

One subject, one primary action, one camera behavior. The LTX-2.3 prompting documentation recommends keeping prompts focused. If you need multiple elements, structure the prompt so they occur at different times in the clip, not simultaneously.

Example

Conflicting: "A fast action sequence with slow motion and dramatic freeze frames" (conflicting speed descriptors)

Fixed: "A fighter lands a punch in slow motion. The impact frame holds for a beat, then the clip resumes at normal speed as they pull back."

Mistake 4: Ignoring Camera Type and Position

The Problem

Without camera specification, the model picks a camera type and position consistent with the scene. This is usually a mid-level, stationary or gently drifting camera. It produces functional but visually generic output. If you have a specific visual style in mind — low angle, aerial, close-up, handheld — omitting it means the model won't produce it.

The Fix

Include camera type, position, and movement in every prompt. LTX-2.3 supports camera direction through prompt language. Relevant camera descriptors: "low angle," "aerial," "close-up," "tracking shot," "handheld," "static camera," "dolly in," "pan left."

Example

Generic: "A chef prepares food in a restaurant kitchen"

Specified: "A chef dices vegetables in a busy restaurant kitchen. Close-up shot on the hands and knife. The camera remains static. Warm overhead lighting. Background activity is blurred."

Mistake 5: Over-Prompting With Too Many Elements

The Problem

Prompts that describe five subjects, three camera behaviors, and detailed background activity exceed what the model can reliably satisfy. Video generation models have a finite capacity for conditioning. Overloading the prompt causes the model to satisfy the most statistically common subset of conditions while ignoring less-common modifiers.

The Fix

Prioritize and simplify. A focused prompt with one subject, clear action, and a single distinct visual quality consistently outperforms a complex prompt that lists every detail. Generate separate clips for complex scenes and combine them in post-production. LTX-2.3 provides the Retake pipeline specifically for fixing specific segments without regenerating entire clips.

Mistake 6: Generating Without Image Conditioning for Character-Specific Scenes

The Problem

When you need a specific character, environment, or object to appear consistently, text description alone is insufficient. The model generates a plausible instance of whatever you describe, not a specific instance. Two clips generated from the same character description will produce different characters unless conditioning enforces consistency.

The Fix

Use image conditioning to anchor the generation to a reference visual. LTX-2.3 pipelines support automatic prompt enhancement tools designed to improve prompt quality. The TI2VidTwoStagesPipeline accepts an image input that replaces the first frame with a specific reference, propagating that visual identity through the generated clip. For multi-clip consistency, use the same reference image as conditioning across all clips in the sequence.

Mistake 7: Not Using Prompt Enhancement Tools

The Problem

Raw prompts often lack the level of detail that video generation models need to produce high-quality output. Most users write prompts that describe what they want to see but omit visual qualities, lighting conditions, spatial relationships, and motion specifics that improve output quality.

The Fix

LTX-2.3 pipelines support automatic prompt enhancement using a Gemma language model. The enhancement step expands your prompt with relevant visual detail before it's passed to the video generation model. This is enabled by default and improves output quality for most prompts without requiring any changes to how you write them.

You can also manually improve prompts by following the structure outlined in the pipeline documentation: scene description, then subject action, then camera behavior, then visual quality descriptors. This order mirrors how the model was trained to process prompt content.

Mistake 8: Ignoring Additional Control Tools

The Problem

When text prompts don't produce the creative control you need, many users continue iterating on prompt wording rather than using the structural control tools available. This leads to many generations with incremental adjustments that may never produce the specific result you're targeting.

The Fix

For more precise creative direction, LTX-2.3 also offers image conditioning (anchoring the first frame to a reference image), IC-LoRA control modes (using pose skeletons, depth maps, or edge maps from existing video as structural references), and fine-tuning (training a LoRA on specific visual content for character or style consistency). These tools address structural control problems that prompt wording alone can't solve. For prompt structure and examples specific to LTX-2.3, see the prompting guide linked at the top of this post.