Published
2025. 10. 10.
Author
ZAKE SHIM
Prompt engineering is the process of finding optimal prompt wording through iterative revisions and submissions.
Prompt engineering is the process of finding optimal prompt wording through iterative revisions and submissions.
Prompt Engineering
Prompt engineering is the process of finding optimal prompt wording through iterative revisions and submissions. Since the quality of AI responses is largely dependent on the completeness of the prompt, the user aims to achieve a higher probability of generating the desired image.
Prompt Buildup
1. Iterative Feedback Loop through Prompt Engineering: Generative AI models can produce a variety of outputs for the same request. When the user repeatedly submits a specific prompt, the AI model performs the same type of inference task, resulting in a result that better matches the user's expectations. This process allows the user to select from a variety of outputs or make further adjustments to achieve better output quality.
This allows the user to select from a variety of outputs or make further adjustments to achieve increasingly better results. This creates the impression that the image inference performance is improving through interaction with the AI, and the user can expect clearer and more specific outputs through the variety of outputs.
2. Iterative Prompting: This process involves continuously revising and submitting prompts to encourage the AI to produce better results. This iterative adjustment (prompt buildup) optimizes AI output.
3. Prompt Refinement:
This process further enhances the prompt by replacing meaningless wording or reference images with details about the desired scene and description. Through active user intervention and control, AI becomes a more sophisticated expression tool. This allows it to embody details that AI cannot express on its own. We will explore specific examples in the next chapter.
As an example, let's look at the process of generating an image of a "car explosion on the road":
Basic prompt: "car explosion on the road"
Refined + refined prompt: "dramatic car explosion on a city street, shockwave rippling through the air, shattered glass fragments suspended mid-air, cracked asphalt, intense orange flames, thick black smoke, debris scattered, motion blur, cinematic lighting, high detail,"
From this simple description of a situation:
Physical effects (shockwave, glass shards, asphalt cracks)
Visual elements (flame color, smoke texture)
Cinematic direction (motion blur, lighting)
Quality specifications (high resolution, detail)
By adding specific prompts (prompt build-up), you can create a more dramatic and realistic image.
More important than the latest AI tools is the ability to select images with a clear visual goal based on the user's imagination and to control the AI's image inference by writing appropriate prompt text to achieve the desired result. Furthermore, the user's discernment and stamina to select and correct AI-generated images for errors are essential for utilizing AI.
Prompt wording, a combination of parts of speech used to derive the user's desired image, defines the essence, character, nuance, theme, and look of the object imagined and expressed through the brain.
We live in a world where this unprecedented tool allows all users to express their imagined images with specific and detailed prompt wording.
Users who simply overlook the importance of prompt wording and focus on AI performance, or who primarily use image prompts, often lack a solid foundation and easily encounter limitations in control and expression. Therefore, we recommend that users prioritize text prompt wording, develop solid writing skills, and organize their own prompt wording in the form of an asset document.
Well-crafted text prompts are sustainable digital assets that are directly proportional to the advancement of AI technology.
When this prompt writing ability is combined with the latest AI features and image prompt selection capabilities, it's like possessing a personalized and effective tool that's completely different from that of a simple user.
Prompt Engineering
Prompt engineering is the process of finding optimal prompt wording through iterative revisions and submissions. Since the quality of AI responses is largely dependent on the completeness of the prompt, the user aims to achieve a higher probability of generating the desired image.
Prompt Buildup
1. Iterative Feedback Loop through Prompt Engineering: Generative AI models can produce a variety of outputs for the same request. When the user repeatedly submits a specific prompt, the AI model performs the same type of inference task, resulting in a result that better matches the user's expectations. This process allows the user to select from a variety of outputs or make further adjustments to achieve better output quality.
This allows the user to select from a variety of outputs or make further adjustments to achieve increasingly better results. This creates the impression that the image inference performance is improving through interaction with the AI, and the user can expect clearer and more specific outputs through the variety of outputs.
2. Iterative Prompting: This process involves continuously revising and submitting prompts to encourage the AI to produce better results. This iterative adjustment (prompt buildup) optimizes AI output.
3. Prompt Refinement:
This process further enhances the prompt by replacing meaningless wording or reference images with details about the desired scene and description. Through active user intervention and control, AI becomes a more sophisticated expression tool. This allows it to embody details that AI cannot express on its own. We will explore specific examples in the next chapter.
As an example, let's look at the process of generating an image of a "car explosion on the road":
Basic prompt: "car explosion on the road"
Refined + refined prompt: "dramatic car explosion on a city street, shockwave rippling through the air, shattered glass fragments suspended mid-air, cracked asphalt, intense orange flames, thick black smoke, debris scattered, motion blur, cinematic lighting, high detail,"
From this simple description of a situation:
Physical effects (shockwave, glass shards, asphalt cracks)
Visual elements (flame color, smoke texture)
Cinematic direction (motion blur, lighting)
Quality specifications (high resolution, detail)
By adding specific prompts (prompt build-up), you can create a more dramatic and realistic image.
More important than the latest AI tools is the ability to select images with a clear visual goal based on the user's imagination and to control the AI's image inference by writing appropriate prompt text to achieve the desired result. Furthermore, the user's discernment and stamina to select and correct AI-generated images for errors are essential for utilizing AI.
Prompt wording, a combination of parts of speech used to derive the user's desired image, defines the essence, character, nuance, theme, and look of the object imagined and expressed through the brain.
We live in a world where this unprecedented tool allows all users to express their imagined images with specific and detailed prompt wording.
Users who simply overlook the importance of prompt wording and focus on AI performance, or who primarily use image prompts, often lack a solid foundation and easily encounter limitations in control and expression. Therefore, we recommend that users prioritize text prompt wording, develop solid writing skills, and organize their own prompt wording in the form of an asset document.
Well-crafted text prompts are sustainable digital assets that are directly proportional to the advancement of AI technology.
When this prompt writing ability is combined with the latest AI features and image prompt selection capabilities, it's like possessing a personalized and effective tool that's completely different from that of a simple user.
Next Projects
