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AI Literacy: An Introduction to Generative AI for Non-Majors

AI literacy refers to 'the ability for general users, not just technicians, to effectively utilize AI in their work and daily lives,' and 'the competency to critically evaluate AI.' This is a practical, literacy-focused lecture, designed for non-majors to understand, covering key knowledge for effectively 'utilizing' AI: from prompt engineering → understanding security → expansion → automation → to language model structure.

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71 learners

  • pronist
ai활용
ai-리터러시
chatgpt
프롬프트-엔지니어링
ChatGPT
prompt engineering
Generative AI
AI Agent

What you will gain after the course

  • You can build your own AI agent and welcome AI as a colleague, not a tool.

  • I can understand the technology that makes AI 'obedient'.

  • You can understand why AI lies and how to prevent it.

  • You can configure workflows and delegate tasks through AI.

  • The prompt's journey to us can be understood.

AI Literacy: An Introduction to Generative AI for Non-Majors

This course is an "AI Literacy" course that provides essential knowledge for the generative AI era and serves as the foundation for advanced learning. Rather than being a technology-focused course for developers, it can be viewed as a foundational generative AI liberal arts course for non-majors, targeting students and working professionals. Since this is a foundational course, taking it before other AI utilization courses will greatly help improve your understanding.

AI Literacy

AI literacy refers to 'the ability for general users, not just technical experts, to effectively utilize AI in their work and daily life', as well as 'the capacity to critically evaluate AI'. This includes critical thinking that involves judging the reliability of information generated by generative AI and considering ethical issues related to AI usage.

So many AI services, do I need to learn them all?

The AI service landscape is currently like a battlefield. Various AI services are striving to achieve a dominant position in the market. However, we don't have time to understand and grasp all of these services. Even while studying the concepts and usage of a specific AI service, yet another new service emerges.

It's not easy to thoroughly use and compare AI services in the same field. Moreover, with so many services available, the subscription costs can't be taken lightly either. We need to be selective and focused, concentrating on the representative AI services in each field.

We first focus on ChatGPT, the most representative generative AI service. Generally, what you can do with ChatGPT can often be done with Gemini, Claude, and Grok as well. The reverse is also true. Well-known general-purpose generative AI companies like OpenAI and Google are competitively launching services. This includes not only general chatbots, but also specialized coding agents for live coding and multimodal capabilities that can process various types of data. Therefore, by focusing on representative services and learning about them, you'll also be able to identify similar services from competitors.

In this course, we have included content that allows you to naturally learn and use related concepts, excluding specialized fields that require domain knowledge such as design, video, music, and comics. The AI services used in the course are composed of services with low entry barriers and difficulty levels that even non-experts can sufficiently use.

The core of generative AI services is AI agents. When we make requests through prompts, AI agents within the service handle various tasks such as writing text, creating code, designing, and drawing pictures. In this course, rather than flashily demonstrating various tools, we will learn the fundamentals of agent components, creation, and usage, enabling you to use agents provided by various services more easily.

What kind of lecture is this?

"AI Liberal Arts Lectures Easily Explained by a Developer for Working Professionals and Students"

  • "AI Literacy: Introduction to Generative AI for Non-Majors" is an AI course easily explained by a developer for working professionals and students. While there are quite a few courses that cover individual topics such as automation and vibe coding, courses that cover a broad and shallow range for generative AI beginners are not easy to find, so I decided to create one myself.

  • This is a literacy education designed so that even non-experts can understand the core knowledge for effectively 'utilizing' AI: prompt engineering → security → automation → language model architecture. In particular, the language model architecture section is an advanced course suitable for those who want to go beyond simply using generative AI and take a comprehensive look at the overall structure of language models.

  • We focus on AI "technology" literacy for understanding, utilizing, and collaborating with generative AI like ChatGPT. The goal is not narrow and deep technical understanding of specific topics, but rather understanding the generative AI ecosystem and tools at a broad but somewhat shallow level to build a solid foundation. This will open new perspectives on utilizing generative AI.

  • It covers most of the generative AI learning roadmaps including AI Agents Roadmap and Prompt Engineering Roadmap. However,This course is sufficient as a start and foundation for generative AI learning, but it cannot be the end. You need to continue studying.

  • Service-centered AI courses generally have short validity periods. Especially when new models are released or versions are updated, they often become useless. This course doesn't obsess over services but focuses on learning the fundamentals of generative AI to prevent knowledge from easily becoming obsolete.

You'll learn this kind of content

  1. Prompt Engineering ― How to Design Questions That AI Can Easily Understand

  2. Security ― How to Use AI Safely

  3. Prompt Expansion ― Utilizing Personalized Memory, RAG, Code-based Prompts, and Multimodal Models

  4. Automation ― Configure automated workflows by connecting AI with external services


  5. Language Models ― Understanding key components that make up language models such as learning, transformers, and attention


Course Features

Practical-focused structure for non-experts

Literacy education that explains even language model structures

This course is designed for students, office workers, and general users who are not developers, with a harmonious combination of theory and hands-on practice to learn various applications and technical concepts discussed in generative AI. The course proceeds with reviewing course materials together and directly practicing them.

I chose a structure that easily explains the principles of language models such as GPT, BERT, Transformers, and Attention. While diagrams do appear for this purpose, there are no complex code, mathematical concepts, or formulas involved in explaining them.

From Automation to Orchestration

Compatible with various models including ChatGPT, Claude, and more

We'll go beyond simple prompt engineering to expand into automation and orchestration to create AI Agents. Using GPTs, MCP, Make, AutoGen Studio, and more, we'll get a feel for what kinds of tasks can be accomplished with generative AI in ChatGPT and beyond.

Prompt engineering, automation and orchestration, and language models are not content limited to specific services or models. They can be applied to future models and services as well, so the knowledge doesn't easily become diluted.

Supplies

It's not over yet

  • After taking this course, it would be good to expand by studying deep knowledge on specific topics such as LangChain, AI orchestration, PromptOps.

  • If you have an understanding of coding and development, it would be good to move forward with LangChain for LLM application development and fine-tuning to make language models into domain experts. However, since these are specialized knowledge areas, there is a lot of technical content you need to know.

  • For non-majors, try building multi-agent systems and workflows using various AI orchestration and automation services like Make, Zapier, n8n, and Flowise.

  • It's also good to try prompt testing using tools like OpenAI Playground, PromptLayer, and Promptfoo.

Recommended for
these people

Who is this course right for?

  • Want to try AI, but lost on where to begin?

  • Those who used ChatGPT, yet found results inconsistent every time.

  • Those looking to reduce repetitive tasks with AI at work

  • Non-engineer students and professionals wanting to understand AI automation flow

  • Anyone worried about AI security/accuracy?

Need to know before starting?

  • Experience using generative AI services such as ChatGPT, Gemini, Claude

Hello
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前 소프트웨어 백엔드 개발자이자 現 프리랜서 생성형 AI 소프트웨어 강사로 활동하고 있습니다. ChatGPT, 프롬프트 엔지니어링 등 생성형 AI 활용에 관심있습니다. 예비창업자, 기획자와 같은 실무자를 대상으로 강의합니다. AI 전문가가 아니더라도 창업과 실무에서 AI를 활용하여 좋은 성과를 낼 수 있도록 돕습니다.

소셜

Github: https://github.com/pronist
BrunchStory: https://brunch.co.kr/@pronist
Email: pronist@naver.com
Tech blog: https://pronist.tistory.com

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Curriculum

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26 lectures ∙ (8hr 15min)

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