Quick Guide to Prompt Engineering: Create Image and Video Prompts for ChatGPT, VEO, Kling and Higgsfield
- Nexxant
- Feb 20
- 11 min read
1. What Is Prompt Engineering and Why Is It Exploding?
If you recently searched for “what is prompt engineering”, you probably noticed something interesting: the term has become global, crossing borders and industries.
Prompt engineering is the practice of structuring instructions strategically to extract better results from artificial intelligence models. It is not just about writing commands. It is about designing them with clarity, structure, and intention.
In simple terms:
Prompt engineering is the skill of directing AI systems to produce predictable, controlled, and professional outputs.
With the rise of tools like ChatGPT and video models such as Veo, Kling, and Higgsfield, this skill has moved from experimental curiosity to professional necessity.
Today, mastering ai prompt engineering allows you to:
Generate hyper-realistic images
Produce cinematic videos with camera control
Structure immersive audio design
Create scalable marketing visuals
Automate creative workflows
The question is no longer what is prompt engineering? The real question is:
How do you use prompt engineering professionally?
Is Prompt Engineering Programming?
Not exactly. But it is also not casual writing.
It combines:
Semantic clarity
Creative direction
Technical structure
Context control
Variable management
It is closer to directing a film scene than writing a sentence.
Compare:
“Man walking in a city.”
Versus:
“Middle-aged architect walking under heavy rain in a neon-lit Tokyo street at night, cinematic atmosphere, 35mm lens, volumetric lighting, realistic reflections.”
The second version controls the output.That control is prompt engineering.
You take control.
2. Why Prompt Engineering Became a Professional Skill
Artificial intelligence is now embedded into workflows across:
Marketing
Design
Architecture
Media production
Software development
Engineering
Companies do not just want to use AI.They want predictable, scalable results.
This is where prompt engineering becomes a competitive advantage.
2.1 Market Demand and Salaries
The demand for professionals skilled in prompt engineering is growing rapidly in both the United States and internationally.
United States
In the U.S., salaries for prompt engineers typically range between:
$100,000 to $130,000 per year on average
Senior or specialized roles can reach $200,000+ annually
Some high-end tech positions have reported offers exceeding $300,000 per year
These roles often sit at the intersection of AI research, product development, and creative automation.
Emerging Markets
In Brazil and other emerging markets, the field is still developing, but compensation is rising:
Entry-level roles: approximately up to $2,000 per month
Mid-level to specialized positions: up to R$3,000+ per month
As companies increasingly adopt AI systems, demand for structured prompt engineering expertise is expected to grow significantly.
2.2 A New Digital Literacy
Just as English became essential in the global economy, understanding prompt engineering is becoming a form of modern digital literacy.
It is not about using AI casually. It is about directing AI strategically.
Professionals who understand structure, style control, and technical instruction design gain a measurable competitive edge.
3. Professional Prompt Engineering Structure for Images
When it comes to AI image generation, prompt structure is what separates an ordinary result from an editorial- or cinematic-quality image.
Image prompt engineering is, essentially, structured visual direction.
If you are looking for a practical prompt engineering guide or best pratices, this is where structure matters most.
3.1 The Architecture of an Image Prompt
A professional image prompt typically contains nine layers:
Subject — Who or what appears in the image
Detailed description — Appearance, texture, age, characteristics
Action or pose — What is happening
Environment — Where the scene takes place
Lighting — Type and direction of light
Camera — Angle and lens
Visual style — Editorial, hyper-realistic, sci-fi, minimalist
Textures and realism — Pores, fabrics, reflections, materials
Aspect ratio — 1:1, 9:16, 16:9
This structure works extremely well with ChatGPT for image generation.
3.2 Professional Template for Image Prompts
[Type of image] of [subject]
[detailed physical description]
[action or pose]
Environment: [location and context]
Lighting: [type, intensity, direction]
Camera: [angle + lens]
Visual style: [cinematic, editorial, hyperrealistic, minimal]
Textures: [skin detail, material realism, reflections]
Emotional tone: [dramatic, elegant, futuristic]
Aspect Ratio: [1:1, 9:16, 16:9]
3.3 Applied Example — Prompt Engineering in Practice
Ultra-realistic cinematic portrait of a futuristic product designer wearing a structured black jacket with subtle metallic textures, in a static pose.
Environment: minimalist studio with dark matte-finished walls.
Lighting: soft side lighting with a subtle rim light.
Camera: low angle, 50mm lens.
Visual style: high-end fashion editorial photography.
Textures: visible skin pores, fabric micro-details, realistic reflections.
Emotional mood: visionary and intense.
Aspect ratio: 16:9.
Notice the difference between this and a simple: “Futuristic designer in a studio.”
Prompt engineering defines:
Aesthetic control
Predictability of the result
Consistency across multiple generations
And that is what turns prompt engineering into a professional skill. Keep in mind that everything depends on the objective. If details are not important, a simple prompt will do the job. Another point to consider: the more details you provide, the less room there is for the “AI’s creativity.” However, overloading a prompt with excessive detail can also increase the risk of hallucinations and cause the result to spiral out of control.
There are also situations where using simple prompts is a smart strategy. For example, during brainstorming phases aimed at exploring creative possibilities. With proper guidance, the tool can generate multiple perspectives that may be essential in shaping the final version.
4. Professional Prompt Engineering Structure for Video (With Audio)
Video prompt engineering requires more complexity because it includes:
Movement
Temporal progression
Physics
Continuity
Audio
Platforms like Veo, Kling, and Higgsfield respond best to structured prompts.
4.1 Estrutura Universal para Prompt de Vídeo
1. Subject and Action:
[Who or what + what is happening]
2. Camera and Motion:
[angle, tracking, dolly, drone, handheld]
3. Visual Style:
[cinematic, documentary, sci-fi, hyperrealistic]
4. Lighting and Atmosphere:
[type of light + emotional tone]
5. Setting:
[detailed environment]
6. Audio Design:
[ambient sounds, soundtrack, sound effects, dialogue]
7. Technical Quality:
[4K, realistic physics, shallow depth of field, film grain]
4.2 Full Template for Video
1. Subject and Action:
[A pessoa ou objeto + o que está fazendo]
2. Camera and Motion:
[ângulo, travelling, dolly, drone, handheld]
3. Visual Style:
[cinematic, documentary, hyperrealistic, sci-fi]
4. Lighting and Atmosphere:
[tipo de luz + clima emocional]
5. Setting:
[ambiente detalhado]
6. Audio Design:
[sons ambientes, trilha sonora, efeitos sonoros, diálogo opcional]
7. Technical Quality:
[4K, realistic physics, shallow depth of field, film grain]
4.3 How to Structure Audio in the Prompt
Most people ignore this part. Professionals do not. Audio Design may include:
🎧 Ambient Sounds
Wind
Rain
Industrial hum
Crowd murmur
🎼 Soundtrack
Minimal piano
Dark ambient
Orchestral build
Synthwave
🔊 Sound Effects
Footstep echo
Digital glitch
Metallic resonance
🎙 Voiceover
Calm documentary voice
Urgent news tone
Whispered dialogue
The trend is that more and more AI tools will include audio in their outputs, but today not all platforms support it.
4.4 Applied Example — Complete Cinematic Video
1. Subject and Action:
A futuristic engineer walking slowly through a neon-lit corridor while holographic screens float around him.
2. Camera and Motion:
Slow tracking shot from the side, smooth cinematic movement.
3. Visual Style:
Cyberpunk realism with high-end film aesthetics.
4. Lighting and Atmosphere:
Soft volumetric blue lighting with reflective metallic surfaces.
5. Setting:
Advanced research facility in the year 2045.
6. Audio Design:
Low ambient electronic soundtrack with subtle bass pulses, soft mechanical hum in the background, slow echoing footsteps synchronized with movement.
7. Technical Quality:
Ultra-detailed textures, realistic physics, 4K, shallow depth of field.
Result:
4.5 Advanced Prompt Engineering Hack for Video
Add control over sound intensity:
“Music builds gradually over 5 seconds.”
“Background audio slightly lower than dialogue.”
“Echo increases as the camera approaches the subject.”
This level of detail transforms AI into a true cinematic production tool.
Another widely used technique for creating videos is to first generate the image. Then, when producing the video, use that image as the base (the first frame). This approach often delivers even better results, especially in cases where you want to create videos by providing both the opening and ending scene images (Kling has this feature natively — allowing video generation using both an initial and a final frame).
Note: Be careful when using initial and final frames. The final frame must align with the scene’s dynamics in the video (for example, object or character positioning, camera movement, etc.), otherwise the tool may struggle to produce a coherent transition.
5. Practical Differences Between ChatGPT, VEO, Kling and Higgsfield
One of the most common mistakes in prompt engineering is using the same structure for every tool. Although the core concept is the same — structuring instructions strategically — each platform responds better to specific types of detail.
Understanding these differences dramatically improves results.
5.1 Prompt Engineering in ChatGPT
Best for:
Concept image generation
Moodboards
Thumbnails
Illustrations
Fast creative exploration
What works best:
Clear block structure
Detailed visual descriptions
Lighting and lens control
Explicit style definition
Less critical:
Complex physics control
Motion continuity
ChatGPT performs very well when prompts are organized, but it does not require the same level of technical detail as models focused exclusively on video.
5.2 Prompt Engineering in Veo
Best for:
Cinematic storytelling
Refined camera movement
Scenes with temporal progression
Rhythm control
What works best:
Structured block division
Clear motion description
Implicit duration control
Sound atmosphere indication
Veo performs better when the prompt describes:
Beginning of the action
Scene progression
Camera movement
Sound build
This is where the Audio Design block makes the biggest difference.
5.3 Prompt Engineering in Kling
Best for:
Human realism
Natural body movement
Facial expressions
Dramatic scenes
What works best:
Detailed posture descriptions
Natural physical interactions
Human movement rhythm
Micro-expressions
If the focus is acting, emotion, or bodily realism, Kling tends to respond better to detailed human descriptions.
5.4 Prompt Engineering in Higgsfield
Best for:
Advanced cinematic language
Sophisticated scene composition
Complex camera direction
Stylized atmosphere
What works best:
Lens type references
Movements like dolly, crane, tracking
Depth of field control
Contrast and texture description
Higgsfield rewards prompts that read like a technical film script.
6. Prompt Engineering Best Practices — Professional Guidelines
If you searched for “prompt engineering best practices,” these are the principles that truly make a difference.
6.1 Be Specific, but Structured
It is not about writing more. It is about writing with intention.
❌ “An elegant woman in a studio.”
✅ “Editorial fashion portrait of an elegant woman in a minimalist studio, soft side lighting, 85mm lens, realistic skin texture, sophisticated atmosphere.”
6.2 Control Technical Variables
Include whenever possible:
Lens type (35mm, 50mm, 85mm)
Camera angle
Lighting type
Level of realism
Texture
Image aspect ratio
This reduces randomness.
6.3 Use Organized Blocks
Structuring the prompt into sections improves:
Clarity
Reproducibility
Future adjustments
Scalability
Professional prompt engineering is not a long paragraph. It is architecture.
6.4 Think Like a Director, Not a User
A user asks. A director specifies.
Instead of:
“Make a futuristic video.”
Think:
Who is in the scene?
How does the camera move?
What is the atmosphere?
What is the rhythm?
Is there ambient sound?
Is there a soundtrack?
This mindset completely changes the result.
6.5 Iteration Is Part of the Engineering
Prompt engineering is not about “getting it right the first time.”
It is:
Generate
Adjust
Refine
Restructure
Professionals use numbered prompt versions and adjust specific blocks only.
7. Common Mistakes That Sabotage Prompt Engineering (And How to Avoid Them)
If you have tried creating images or videos with AI and felt “it did not look like I imagined,” the issue may not have been the tool — but the prompt.
Structural mistakes compromise results and create rework.
Let’s look at the main ones.
7.1 Being Too Generic
This is mistake number one.
❌ “A futuristic video in a city.”❌ “A professional marketing image.”
These prompts hand full creative control to the AI.
Professional prompt engineering requires specificity:
Who is in the scene?
What is the environment?
What is the mood?
What lens is being used?
What type of lighting?
Is there a soundtrack?
The vaguer the instruction, the more random the result.
7.2 Mixing Incompatible Styles
Another common mistake is combining conflicting references:
❌ “Cinematic minimalist hyper-realistic cartoon neon vintage documentary.”
This creates visual inconsistency.
Practical rule:
Choose one dominant style and, at most, one complementary secondary style.
7.3 Ignoring Scene Physics (Especially in Video)
In video, this becomes even more evident.
If you request:
“Person running quickly while the camera slowly rotates 360°.”
There is physical incoherence.
Models like Veo and Kling respond better when motion makes sense within the scene.
Prompt engineering also involves logical coherence.
7.4 Not Controlling Audio
Many creators completely forget the audio block.
But audio defines:
Rhythm
Tension
Emotion
Immersion
If you do not specify:
Ambient sounds
Soundtrack
Intensity
Voiceover
The AI decides for you — or delivers something too neutral.
7.5 Writing Prompts as a Long Paragraph
Professional prompt engineering is not a disorganized paragraph.
Structuring in blocks like:
Subject and Action
Camera and Motion
Audio Design
increases predictability and facilitates iteration.
This is especially important in platforms like Higgsfield, which respond better to structured commands.
7.6 Not Iterating
Critical mistake.
Professionals do not write one prompt and stop.
They:
Generate
Evaluate
Adjust specific block
Refine again
Prompt engineering is a process, not a guess.
8. Recommended Learning Resources — Google Certifications on Coursera
For those who want to deepen their knowledge in AI and prompt engineering with market-recognized certification, two official Google specializations on Coursera are highly recommended:
🎓 Google AI Essentials Specialization
👉 A program that introduces the fundamentals of artificial intelligence, including AI tool usage, productivity applications, and basic prompting techniques.
Structured as a multi-course series with practical projects and certification.
Ideal for beginners who want to understand how AI works, its applications, and how to use it in real-world scenarios.
🧠 Google Prompting Essentials Specialization
👉 A course focused on effective prompt creation techniques, teaching how to formulate clear and efficient instructions for generative AI models in five simple steps.
Covers fundamentals through advanced best practices to maximize output quality.
Useful for professionals and creators who want to master prompting skills.
💻 Anthropic’s Interactive Prompt Engineering Tutorial
👉 An interactive prompt engineering tutorial offered by Anthropic, focused on practical experimentation and real examples, available on GitHub.
Ideal for those who want hands-on experimentation with different prompt structures and understand the impact of each element.
The repository contains several .ipynb files, so you will need a program capable of opening them. Options include:
Using Google Colab (online)
VS Code with Python extensions
Installing Python and Jupyter Notebook
Why Include These Courses in Your Prompt Engineering Journey?
✔ They provide a structured learning path recognized by Google and the market.
✔ They complement this guide with theoretical and practical foundations.
✔ They allow you to add certifications and portfolio credentials to your resume or professional profile.
Additionally, Nexxant has an extensive prompt library covering multiple areas, including image generation. There is also a complete AI video creation guide, where the parameters that should be included in a prompt are explained in greater detail.
Links:
Prompt Engineering Is Creative Direction Applied to AI
What began as curiosity has become a strategic skill.
Today, mastering prompt engineering means:
Reducing rework
Increasing quality
Controlling aesthetics
Scaling production
Integrating AI into professional workflows
It is not just about using tools like ChatGPT.
It is about directing intelligent systems with clarity, intention, and architecture.
Those who master:
Image structure
Video structure
Audio design
Technical control
Strategic iteration
turn AI into a productive asset. And this is only the beginning.
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