Where does the difference come from between people who use AI well and those who do not? Many answer, “Do they know good prompts?” In reality, the issue is different. The core is not a few prompt sentences, but whether I can say what I want with context and criteria included.
The Ildangbaek video “Human intelligence expressed delicately through language: the beginning of AI prompt engineering” shows this point well. The starting point is the book *AI Language Lessons for Intellectual Conversation*, but the conversation is less a book introduction than a question about language sense in the AI era. For Korean users, the question is even more important: when talking with AI in a language rich in omission and nuance, what must we make clearer?
## The AI-use gap comes from “language resolution,” not tool operation
A good prompt begins with organizing thoughts and criteria before sentence technique.
Many people first say to AI: “Organize this for me.” “Do not make it too long.” “Do not sound stiff.” “Do not draw an image; just show the prompt.”
Between people, this often works because we read surrounding context, facial expressions, previous conversations, organizational culture, and tone. AI, however, guesses context the user did not provide. When the guess is right, it is convenient; when it is wrong, the result goes off track.
Prompt guides from OpenAI and Anthropic commonly emphasize clear instructions, enough context, and the desired output format. A good prompt is not a magic sentence. It is a sentence that reduces what AI must guess.
The key difference appears here. People who use AI well do not simply write longer questions. They structure context: purpose, reader, constraints, examples, preferences rather than bans, output format, and verification criteria.
## Why Korean is harder for AI
Korean omissions and nuance require clearer context explanation for AI.
One of the video’s most interesting points is the high-context nature of Korean. Korean often omits subjects and objects. A single particle changes focus. Honorifics may be processed on the surface, but sarcasm and irony are different issues.
For example, sentences equivalent to “Cheolsu went to school” with different Korean particles may look similar but have different focus. “It is okay” may mean acceptance or refusal. Mixed emotions such as relief and regret vary by context.
Humans read these differences from the situation. AI mostly receives text. Korean users therefore need to provide more context. “Do it appropriately” is convenient for humans but information-poor for AI.
This also appears in translation. Anthropic’s interpretability research suggests large language models can connect multilingual inputs to shared internal concept spaces. But that does not mean Korean nuance is perfectly preserved. Emotion, omission, irony, and speaker intention can be lost when moving between languages.
## Prompt engineering is not “asking well”; it is operating a system
In organizations, prompt engineering goes beyond asking questions and becomes quality and operations design.
The video distinguishes prompts from prompt engineering. Everyday users can ask AI conversationally. But business systems, customer service, automation, and content pipelines are different.
Prompt engineering is not simply “the skill of asking beautifully.” It examines how models differ in response tendencies, analyzes why wrong answers appear, designs structures that reduce cost, connects multi-step work reliably, and controls consistency.
Writing may require creativity, but customer notices or legal and policy guidance should not vary every time. In such cases, generation settings like temperature, example-based output, verification steps, and retry conditions are needed.
Prompt engineering is therefore both a language skill and an operations skill. It starts from an individual’s questioning habits, but in organizations it expands into quality and cost management.
## “Do this” is stronger than “Do not do that”
For AI, it is more stable to give desired direction and criteria than only prohibitions.
One practical tip repeated in the video is to use positive statements rather than negatives. “Use everyday words” is better than “Do not use technical terms.” “Write within three sentences per paragraph” is better than “Do not make it long.” “Write as a short explanatory passage” is clearer than “Do not use a list.”
AI does not always handle negative instructions reliably. In image, video, or multimodal models, negative words can blur the desired result. Even in text models, “do not” can place the banned element at the center of context.
| Common request | Better request |
|—|—|
| Do not write too difficultly | Use everyday words a middle-school student can understand |
| Do not make it long | Explain only three key points within 600 characters |
| Do not sound like AI | Mix short and long sentences and reduce repeated expressions |
| Organize it for me | Organize it in the order of background, key issue, and action items |
| Do not include subjective opinions | Separate confirmed facts from interpretation |
This difference looks small, but the result changes greatly. Reducing the space for AI to guess reduces revision time.
## The productivity debate is about what you delegate, not how much you use AI
AI productivity depends on the ability to divide what AI should handle from what people must judge.
Opinions differ on whether AI truly improves productivity. Some research already observes concrete effects. NBER’s “Generative AI at Work” found that generative AI tools increased average productivity in customer support and especially helped less-experienced workers.
The ILO’s analysis of generative AI and jobs, by contrast, suggests many jobs are more likely to have some tasks automated or assisted than to be fully replaced. This matches the video’s conclusion: AI does not simply eliminate all work; it redistributes components of work.
The question is not “How much do you use AI?” It is whether you can decide what to delegate and what humans should judge. Simple summarization, drafting, format conversion, and repetitive replies are easy to delegate. Reading customer anxiety, judging field context, and cautiously confirming unspoken needs still remain heavily human.
## Five prompt principles for Korean users
### 1. Restore omitted subjects and objects
Before writing “organize it,” state what should be organized, for whom, and for what purpose. Omission is natural in Korean conversation, but it becomes a blank for AI.
### 2. Turn negative statements into positive ones
Say “Use a friendly but not exaggerated tone” rather than “Do not sound stiff.” Goals are more stable than bans.
### 3. Decide the output format first
A table, list, paragraph, report, blog post, email, and presentation script are different outputs. Without a format, AI gives an average answer.
### 4. Separate context and criteria
Give background as background, requirements as requirements, and verification criteria as verification criteria. If everything is mixed into one sentence, AI may miss priorities.
### 5. Do not try to finish in one turn
Good AI use is closer to multi-turn collaboration than a single command. Receive a draft, strengthen criteria, revise again, and verify at the end.
## A prompt is ultimately a conversation habit, not just a technique
UNESCO’s AI competency framework treats AI-era capability not as mere tool operation but as human-centered thinking, ethics, critical judgment, and practical use. Prompts are similar. They are not shortcuts to memorize.
Talking with AI is a process of making your own thinking clearer. If you do not know what you want, AI does not know either. If you do not give criteria, AI produces an average. If you omit context, AI guesses.
That is why the video’s core is deeper than “write better prompts.” Competitiveness in the AI era belongs not to people who know many technical tricks, but to people who can examine their own language and design context.
In that sense, future AI literacy may be a language issue before it is a coding issue, especially for Korean users. The words we naturally omitted, the atmosphere we relied on, and the “do it appropriately” we handed over must all become sentences again in front of AI.
## Further reading
– [Metacognition in the AI Era](https://www.thinknote.co.kr/metacognition-ai-thinking-checklist/)
– [In the AI Agent Era, How Knowledge Workers Must Change](https://www.thinknote.co.kr/ai-agent-valuable-education/)
– [Six Habits of People Who Get Smarter the More They Use AI](https://www.thinknote.co.kr/ai-smarter-use-six-habits/)
## References
– [Ildangbaek video](https://youtu.be/OcmSp1Cn5rg?si=LQk_SQmJmDuqaETH)
– [OpenAI, Prompt engineering](https://platform.openai.com/docs/guides/prompt-engineering)
– [Anthropic, Prompt engineering overview](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview)
– [Anthropic, Tracing the thoughts of a large language model](https://www.anthropic.com/research/tracing-thoughts-language-model)
– [Brynjolfsson, Li, and Raymond, Generative AI at Work](https://www.nber.org/papers/w31161)
– [International Labour Organization, Generative AI and Jobs](https://www.ilo.org/publications/generative-ai-and-jobs-global-analysis-potential-effects-job-quantity-and-quality)
– [UNESCO, AI competency framework for teachers](https://www.unesco.org/en/articles/ai-competency-framework-teachers)
## FAQ
### Are Korean prompts disadvantaged compared with English prompts?
Not always. But Korean relies heavily on omission, particles, honorifics, and context, so AI often has to guess intention. When writing in Korean, it is better to state situation and criteria more clearly.
### Do I have to learn prompt engineering?
Everyday users do not need grand engineering. But if you use AI for work, you need the basic habit of giving purpose, context, output format, and verification criteria.
### Why does “do not do this” often fail with AI?
Negative statements place the prohibited object inside the context. Some models do not reliably reflect the ban. It is usually better to specify the desired behavior in positive terms.
### Can AI writing be made to feel human?
To some degree. Adjusting sentence length, repeated expressions, omissions, rhythm, and concrete situations can reduce a mechanical feel. But AI does not possess real experience or judgment.
### What should humans do in the AI era?
Humans must interpret context, set criteria, and make final judgments. Customer emotion, field situations, organizational tacit knowledge, and ethical judgment remain difficult to standardize fully.
[Original Korean article](https://www.thinknote.co.kr/ai-korean-prompt-literacy/)
Image source: Captured images used in this article are stills from the original YouTube video. They are used for review, commentary, and educational explanation, and copyright remains with the original rights holders and the channel.
The difference between people who use AI well and those who do not—where does it come from? People often answer, “It depends on whether you know good prompts.” In reality, it is a little different. The core issue is not a handful of prompt sentences. It is whether I can say what I want while also including the context and criteria behind it.
The Ildangbaek video “Human Intelligence Expressed Delicately Through Language! The Beginning of AI Prompt Engineering” illustrates this point well. The video begins with the book AI Language Lessons for Intellectual Conversation, but rather than being a simple book introduction, it is closer to a conversation that asks what kind of language sense we need in the AI era. For Korean-language users in particular, there is an even more important question: when we talk with AI in a language like Korean, where omissions and nuance are common, what do we need to say more clearly?
The AI Usage Gap Comes Less from “How to Use the Tool” Than from the “Resolution of Language”
A good prompt begins not with sentence technique, but with organizing your thoughts and criteria.
When many people first use AI, they say things like this:
“Just organize this for me.” “Don’t make it too long.” “Don’t use a stiff tone.” “Don’t draw an image—just show me the prompt.”
Between people, this level of instruction usually works to some extent. That is because we read the surrounding situation, facial expressions, prior conversations, organizational culture, and tone of voice together. But AI guesses the context the user has not provided. If the guess is right, it feels convenient. If it is wrong, the result becomes completely off target.
The prompt guides from OpenAI and Anthropic both emphasize “clear instructions, sufficient context. The desired output format.” Ultimately, a good prompt is not a magic sentence. It is a sentence that reduces the parts AI has to guess.
This is where an important difference appears. People who use AI well are not necessarily people who write longer questions. They are people who structure context. They provide purpose, audience, constraints, examples, preferences rather than only prohibitions, output format, and validation criteria together.
Why Korean Is a More Difficult Language for AI
Korean’s omissions and nuances require clearer explanations of context when working with AI.
One of the most interesting points in the video is the high-context nature of Korean. Korean frequently omits subjects and objects. A single particle can change the focus of a sentence. Honorifics may be handled reasonably well at the surface level. Sarcasm and irony are entirely different matters.
For example, “Cheolsu-neun went to school” and “Cheolsu-ga went to school” may look similar, but their focus is different. “It’s okay” can mean that something is truly okay, or it can mean refusal. “Siwon-seopseop-hada”—a Korean expression that combines feeling refreshed or relieved with feeling sad or regretful—has a different ratio of relief to regret depending on the situation.
People read these differences through the situation. AI mostly receives them as text. That is why Korean users need to provide AI with more context. “Take care of it” is convenient, but from AI’s point of view it is an instruction with too little information.
This issue also appears in translation. Anthropic’s interpretability research shows that large language models can connect inputs from multiple languages to a shared internal conceptual space. But that does not mean Korean nuance is perfectly preserved. In the movement between languages, emotion, omission, irony, and the speaker’s intent can be lost.
Prompt Engineering Is Not “Asking Good Questions”; It Is Managing a System
In organizations, prompt engineering goes beyond asking better questions and becomes a matter of quality and operational design.
The video distinguishes between prompts and prompt engineering. Everyday users can simply ask questions as if they were talking with AI. But the story changes in work systems, customer service, automation, and content production pipelines.
Prompt engineering is not simply “the skill of asking pretty questions.” It involves looking at how answer tendencies differ from model to model. It analyzes why wrong answers emerged. It designs structures that reduce cost. It connects multi-step tasks reliably. It controls the consistency of results.
For example, writing requires creativity, but customer guidance copy or legal and policy guidance becomes problematic if it changes every time. In these cases, generation settings such as temperature, example-based output, validation steps, and retry conditions are needed.
In other words, prompt engineering is a language skill and, at the same time, an operational skill. It begins with an individual’s way of asking questions. In organizations it expands into quality management and cost management.
“Do It This Way” Is Stronger Than “Don’t Do That”
For AI, it is more stable to specify the desired direction and criteria than to state only prohibitions.
One practical tip repeated in the video is to use positive statements rather than negative ones. “Use everyday words” is better than “Don’t use technical terms.” “Keep each paragraph to three sentences or fewer” is better than “Don’t write too much.” “Write it as a short explanatory passage” is clearer than “Don’t write it as a list.”
AI does not always process a user’s negative phrasing reliably. In image, video, and multimodal models in particular, negative words can blur the desired result. Even in text models, saying “don’t do this” can sometimes place the prohibited element at the center of the context.
At work, it is better to change requests like this:
Common request
Better request
Don’t write it too difficult.
Write it in everyday language that a middle school student can understand.
Don’t make it long.
Explain only the three core points within 600 Korean characters.
Don’t make it sound like AI.
Mix short and long sentences, and reduce repeated expressions.
Just organize it for me.
Organize it in the order of background, key issues, and action items.
Don’t include subjective opinions.
Separate verified facts from interpretation.
This difference may look small, but the results change significantly. When you reduce the room AI has to guess, you reduce the time you spend revising.
The Core of the AI Productivity Debate Is Not “How Much You Used It” but “What You Delegated”
AI productivity depends on the ability to decide what to delegate and what humans should judge.
Opinions differ on whether AI actually increases productivity. Still, some studies have already observed concrete effects. The NBER paper “Generative AI at Work” found. Generative AI tools increased average productivity in customer support work, with especially large effects for less experienced employees.
By contrast, the ILO’s analysis of generative AI and jobs suggests. Many occupations are more likely to see some tasks automated or supported than to be completely replaced. This perspective also connects with the video’s conclusion. AI does not necessarily eliminate all work; rather, it redivides the components of work.
The question is not “Do you use AI a lot?” It is the ability to decide what to delegate and what humans should judge. Simple summaries, drafts, format conversions, and repeated responses are easy to delegate to AI. But reading a customer’s anxious feelings, judging field context. Carefully confirming unspoken needs are still largely human responsibilities.
Five Prompt Principles for Korean-Language Users
1. Restore the Omitted Subject and Object
Before writing “Organize this,” write what should be organized, for whom, and for what purpose. In Korean conversation, omission is natural, but for AI it becomes a blank space.
2. Turn Negative Sentences into Positive Sentences
Instead of saying “Don’t write in a stiff way,” say “Write in a friendly but not exaggerated tone.” Giving a goal is more stable than giving only a prohibition.
3. Decide the Output Format First
A table, list, paragraph, report, blog post, email, and presentation script are all different outputs. If you do not set the format, AI produces an average answer.
4. Provide Context and Criteria Separately
Separate the background as background, requirements as requirements, and validation criteria as validation criteria. If you mix everything into one sentence, AI can also miss the relative importance.
5. Do Not Try to Finish Everything in One Turn
Good AI use is closer to multi-turn collaboration than to a single turn. Receive a draft, strengthen the criteria, revise it again, and validate it at the end. This is not a command; it is collaboration.
In the End, Prompts Are Not a Technique but a Habit of Conversation
UNESCO’s AI competency framework sees the abilities needed in the AI era not as simple tool usage. As human-centered thinking, ethics, critical judgment, and practical application. Prompts are the same. They are not something to memorize like keyboard shortcuts.
Talking with AI is a process of making my own thinking clearer. If I do not know what I want, AI does not know either. If I do not provide criteria, AI produces an average value. If I omit context, AI guesses.
That is why the core of the video goes deeper than “Let’s write better prompts.” Competitiveness in the AI era comes not to people who know a lot of techniques. To people who can examine their own language and design context.
To put it a little strongly, future AI literacy may be a language issue before it is a coding issue. This is especially true for Korean-language users. The words we naturally omitted, the things we passed over through atmosphere. The tasks we handed off by saying “take care of it” must all become sentences again in front of AI.
Anthropic, “Tracing the thoughts of a large language model,” View source
Erik Brynjolfsson, Danielle Li, Lindsey R. Raymond, “Generative AI at Work,” NBER Working Paper No. 31161, View source
International Labour Organization, “Generative AI and Jobs,” View source
UNESCO, “AI competency framework for teachers,” View source
FAQ
Are Korean Prompts at a Disadvantage Compared with English Prompts?
It is not accurate to say they are always at a disadvantage. However, Korean relies heavily on omission, particles, honorifics, and context. AI often has to guess the user’s intent. That is why, when writing in Korean, it is better to state the situation and criteria more clearly.
Do I Really Need to Learn Prompt Engineering?
Everyday users do not need to learn grand, formal engineering. But if you use AI for work, you do need the basic habit of providing purpose, context, output format, and validation criteria.
Why Does Telling AI “Don’t Do That” Often Fail?
Negative sentences place the prohibited object inside the context. Some models do not reliably reflect the intention behind the prohibition. That is why it is better to specify the desired behavior positively rather than saying only “don’t.”
Can AI-Written Text Be Made to Sound Human?
To some extent, yes. Adjusting sentence length, repeated expressions, subject and object omission, inversion, rhythm. Concrete situations can reduce the mechanical feeling. However, AI does not actually possess real experience or judgment on your behalf.
What Work Should Humans Take On in the AI Era?
Humans should interpret context, set criteria, and make final judgments. Areas that are difficult to fully standardize in words—such as customer emotions, field situations, an organization’s tacit knowledge. Ethical judgment—still depend heavily on human roles.
Image source: Captured images used in this article are stills from the original YouTube video. They are used for review, commentary, and educational explanation, and copyright remains with the original rights holders and the channel.
This English version is a fuller translation and adaptation of the original Korean article, “AI 스킬 만들기, 파일 3개로 시작하는 Claude·GPT 업무 자동화,” for global readers. The article discusses the importance of creating AI skills, which involves turning prompts into reusable work automation. It highlights the difference between project instructions and skills, and how skills can be used to automate repetitive tasks. The article also provides a step-by-step guide on how to create AI skills using Claude and GPT/Codex, and offers tips on how to review and refine the skills.
The process of creating AI skills involves turning prompts into reusable work automation. This means that instead of writing a new prompt every time, you can create a skill that can be reused multiple times. The article uses the analogy of a recipe and a meal kit to explain the difference between project instructions and skills. Just as a recipe provides a set of instructions for cooking a meal, a skill provides a set of instructions for completing a task.
Difference between Project Instructions and Skills
Project instructions are specific to a particular project and provide a set of rules for completing a task. Skills, on the other hand, are more general and can be applied to multiple projects. Skills can include not only the instructions for completing a task but also the necessary materials, tools, and standards. This means that skills can be used to automate repetitive tasks and improve efficiency.
AI skill package structure.
Why AI Skills are Important Now
AI skills are important now because they can be used to automate repetitive tasks and improve efficiency. The article highlights the difference between early AI systems, which were limited to answering simple questions, and modern AI systems, which can perform complex tasks and make decisions. The article also discusses the role of prompt engineering in creating AI skills, and how it has changed over time.
Role of Prompt Engineering
Prompt engineering involves designing and optimizing prompts to get the best results from an AI system. The article highlights the importance of structuring prompts to get the best results, and how this can be used to create AI skills. The article also provides examples of how prompt engineering can be used to create AI skills, such as creating a skill for generating reports or creating a skill for automating data entry.
SKILL.md as an execution guide.
Basic Structure of AI Skills
The basic structure of AI skills involves creating a set of instructions and materials that can be used to complete a task. The article highlights the importance of creating a clear and concise set of instructions, and how this can be used to create AI skills. The article also discusses the role of references and scripts in creating AI skills, and how these can be used to improve efficiency and accuracy.
SKILL.md: The Execution Manual
SKILL.md is the execution manual for an AI skill. It provides a set of instructions for completing a task, and can include information such as the materials and tools needed, the steps to follow, and the standards to meet. The article highlights the importance of creating a clear and concise SKILL.md, and how this can be used to create AI skills.
references and scripts for AI automation.
References: The Knowledge Base
References are the knowledge base for an AI skill. They provide additional information and materials that can be used to complete a task, such as documents, templates, and scripts. The article highlights the importance of creating a clear and concise set of references, and how these can be used to improve efficiency and accuracy.
Scripts: The Automation Tool
Scripts are the automation tool for an AI skill. They provide a set of instructions that can be used to automate repetitive tasks, such as data entry or report generation. The article highlights the importance of creating clear and concise scripts, and how these can be used to improve efficiency and accuracy.
Claude and GPT workflow automation.
Creating AI Skills: A Step-by-Step Guide
Creating AI skills involves a step-by-step process that includes defining the task, creating the SKILL.md, references, and scripts, and testing and refining the skill. The article provides a detailed guide on how to create AI skills using Claude and GPT/Codex, and offers tips on how to review and refine the skills.
Conclusion: AI Skills are the Assets of the AI Era
AI skills are the assets of the AI era. They can be used to automate repetitive tasks, improve efficiency, and enhance productivity. The article highlights the importance of creating AI skills, and how these can be used to improve business outcomes. The article also provides a checklist for creating AI skills, and offers tips on how to get started.
Related Reading
Continue with these related Thinknote English articles in the Digital Transformation cluster.
This article explains a digital transformation, platform, market-structure, or technology-adoption topic with Korea-specific context and global implications.
How should I use this guide?
Use it to understand market signals and strategic patterns. Combine it with current market data before making business or investment decisions.
