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AI Literacy: 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 practical work and daily lives," as well as the "competency to critically evaluate AI." This is a practice-oriented literacy course designed for non-experts to understand the core knowledge for "utilizing" AI well—covering everything from prompt engineering to security understanding, expansion, automation, and the structure of language models.

(4.7) 22 reviews

121 learners

Level Beginner

Course period Unlimited

AI
AI
ChatGPT
ChatGPT
prompt engineering
prompt engineering
AI Agent
AI Agent
AI
AI
ChatGPT
ChatGPT
prompt engineering
prompt engineering
AI Agent
AI Agent

What you will gain after the course

  • You can configure your own AI agent and welcome AI as a colleague rather than just a tool.

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

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

  • You can configure workflows and delegate tasks through AI.

  • You can understand the flow from when a prompt is entered until it is output to us.

AI Literacy: Introduction to Generative AI for Non-Majors

This course is an "AI Literacy" lecture that provides essential knowledge and a foundation for advanced learning in the era of generative AI. Rather than a technology-centered lecture for developers, it can be seen as a basic liberal arts course on generative AI for non-majors, targeting students and office workers. As it is an introductory course, studying it before taking other AI application courses will greatly help improve your understanding.

AI Literacy

AI literacy refers to the 'ability for general users, not just technicians, to effectively utilize AI in practical work and daily life,' as well as the 'competence to critically evaluate AI.' This includes critical thinking to judge the reliability of information generated by generative AI and to consider ethical issues regarding AI usage.

Countless AI services—do we 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 do not have the 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 often emerges.

It is also not easy to patiently use and compare AI services within the same field. Furthermore, with so many services available, the subscription costs incurred cannot be overlooked. There is even a term called "digital rent." We need selection and focus, and it is necessary to concentrate on the representative AI services in each field.

First, we focus on ChatGPT, the most representative generative AI service. As ChatGPT is used by the most people among generative AI services, it is the absolute fundamental. Furthermore, in most cases, what can be done in ChatGPT can also be done in services like Gemini, Claude, and Grok. The reverse is also true. Widely known general-purpose generative AI companies like OpenAI and Google are competitively releasing services—not just general chatbots, but also specialized coding agents for vibe coding and multimodal capabilities that can process various types of data. Therefore, if you focus on the representative services and get to know them, you will also be able to identify similar services offered by competitors.

In this course, with the exception of specialized fields requiring domain knowledge such as design, video, music, and comics, I have included relevant concepts so that you can naturally learn to use them. The AI services used in the lectures are composed of tools with low entry barriers and difficulty levels, making them fully accessible even for non-experts.

The core of generative AI services is the AI agent. When we make a request via a prompt, the AI agent within the service handles various tasks such as writing text, generating code, designing, and creating images. Rather than focusing on flashily mastering numerous tools, this course aims to help you learn the fundamental components of agents and the basics of creating and using them, so you can more easily utilize agents provided by various services.

What kind of lecture is this?

"An introductory course on generative AI, explained easily by a developer for professionals and students"

  • "AI Literacy: Generative AI Introduction for Non-Majors" is an AI course where a developer provides easy explanations for office workers and students. While there are quite a few courses covering individual topics such as automation or vibe coding, it was difficult to find a course that covers a wide and shallow range for an introduction to generative AI, so I decided to create one myself.

  • This is a literacy education program designed to be understood even by non-experts, covering core knowledge for effectively 'utilizing' generative AI, from prompt engineering → security → automation → to language model architecture. In particular, the language model architecture section is an advanced course suitable for those who wish to go beyond simply using generative AI and gain a comprehensive understanding of how language models are structured.

  • It focuses on AI "technical" literacy, which involves understanding, utilizing, and collaborating with generative AI such as ChatGPT. However, rather than providing a narrow and deep expert-level technical understanding of a specific topic, the goal is to build a solid foundation by understanding the generative AI ecosystem and tools at a broad but somewhat introductory level in order to utilize various AI services in practice.

  • It covers most of the generative AI learning roadmaps, such as the AI Agents Roadmap and the Prompt Engineering Roadmap. However, while this lecture is sufficient as a start and foundation for learning generative AI, it is not the end. You will need to continue studying.

  • AI courses focused on specific services generally have a short shelf life. In many cases, they become obsolete when new models, competing services are released, or versions are updated. Rather than obsessing over specific services, this course focuses on mastering the fundamentals of generative AI to prevent the knowledge you've worked hard to acquire from easily evaporating.

What you will learn

  1. Prompt Engineering ― How to design questions so that AI can easily understand them

  2. Security ― How to use AI safely

  3. Prompt Expansion ― Utilizing personalized memory, RAG, code-based prompts, and multimodal models

  4. Automation ― Connecting AI with external services to configure automation workflows


  5. Language Models ― Understanding core elements that make up language models, such as training, transformers, and attention


Lecture Features

Practical-oriented composition for non-experts

Literacy education that explains even the structure of language models

This course is designed for non-developers, including students, office workers, and general users, and harmoniously combines theory and practice to help you master various usage methods and technical terms used in generative AI. The process involves reviewing the course materials together and engaging in direct hands-on practice.

We have chosen a structure that explains the principles of language models easily, covering GPT, BERT, Transformer, Attention, and more. While diagrams are included for this purpose, no complex code, mathematical concepts, or formulas are used to explain them.

To automation and orchestration

Compatible with various models such as ChatGPT and Claude

We will go beyond simple prompt engineering and expand into automation and orchestration to create AI Agents. By using tools such as GPTs, MCP, Make, and AutoGen Studio, you will get a sense of what can be achieved with generative AI within ChatGPT and beyond.

Content such as prompt engineering, automation and orchestration, and language models is not limited to specific services or models. Because it can be applied to future models and services, this knowledge does not easily become obsolete.

Requirements

It's not over yet

  • After taking this course, it is recommended to expand your studies by gaining deep knowledge in specific topics such as LangChain, AI Orchestration, and PromptOps.

  • If you have an understanding of coding and development, it is also good to move on to LangChain for LLM application development and fine-tuning to turn language models into domain experts. However, as these are specialized fields, 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 such as Make, Zapier, N8N, and Flowise.

  • It is also a good idea to perform prompt testing using tools such as OpenAI Platform, PromptLayer, and Promptfoo. cũng là một ý hay.

Recommended for
these people

Who is this course right for?

  • Those who have used ChatGPT but found the results to be inconsistent every time

  • Those who want to try using AI but feel overwhelmed about where to start.

  • Those who want to reduce repetitive tasks by utilizing AI in practice.

  • Students and professionals who are not technicians but want to understand the flow of AI automation

  • Those who have ever worried about security and accuracy issues when using AI

Need to know before starting?

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

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I am a freelance software developer and generative AI instructor. I am interested in utilizing AI, including ChatGPT and prompt engineering. I teach practitioners such as aspiring entrepreneurs and planners. I help even those who are not AI experts achieve great results by leveraging AI in their startups and practical work.

 

  • Contributing Writer and Journalist for Brand News, National Brand Promotion Agency

  • Partner Instructor at Crowd Academy

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22 reviews

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    It is a satisfying lecture.

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