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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.

(5.0) 9 reviews

78 learners

Level Beginner

Course period Unlimited

ChatGPT
ChatGPT
prompt engineering
prompt engineering
Generative AI
Generative AI
AI Agent
AI Agent
ChatGPT
ChatGPT
prompt engineering
prompt engineering
Generative AI
Generative AI
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 covers "AI Literacy," which provides essential knowledge and a foundation for advanced learning in the era of generative AI. Rather than a technology-centered course for developers, it can be seen as a basic liberal arts course on generative AI for non-majors, including students and professionals. As it is an introductory course, taking it before 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 their practical work and daily lives,' as well as 'the capacity 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 have 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 not easy to patiently use and compare AI services within the same field. Furthermore, with so many services available, the subscription costs can be quite significant. People are even calling it "digital rent." We need to practice selection and focus, concentrating on the representative AI services in each field.

First, we focus on ChatGPT, the most representative generative AI service. Generally, what can be done in ChatGPT can often be done in services like Gemini, Claude, and Grok as well. The reverse is also true. Widely known general-purpose generative AI companies like OpenAI and Google are competitively launching services, ranging from general chatbots to specialized coding agents for vibe coding and multimodal capabilities that can process various types of data. Therefore, by focusing on and learning about these representative services, you will naturally be able to identify similar offerings from 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 in the lessons so that you can learn and use them naturally. The AI services used in this lecture 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 illustrations. Rather than focusing on flashily mastering numerous tools, this course focuses on learning the fundamental components of agents, the basics of creating and using them, and making it easier to utilize agents provided by various services.

What kind of lecture is this?

"An AI liberal arts lecture where a developer explains easily for office workers and students"

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

  • This is a literacy education program designed for non-experts to understand the core knowledge for effectively 'utilizing' AI, covering 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 look at how language models are structured.

  • It focuses on AI "technical" literacy, which involves understanding, utilizing, and collaborating with generative AI like ChatGPT. The goal is to build a solid foundation by understanding the generative AI ecosystem and tools at a broad, albeit slightly shallow level, rather than a narrow and deep technical understanding of a specific topic. It will open up new perspectives on how to utilize generative AI.

  • 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 primarily on specific services generally have a short shelf life. In many cases, they become obsolete when new models, competing services, or version updates are released. Instead of obsessing over specific services, this course focuses on mastering the fundamentals of generative AI to prevent knowledge from quickly becoming outdated.

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 the core elements that constitute language models, such as training, transformers, and attention


Lecture Features

Practical-oriented composition for non-experts

Literacy education that even explains the structure of language models

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

I have chosen a structure that explains the principles of language models easily, such as GPT, BERT, Transformer, and Attention. 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 gain a sense of what can be achieved with generative AI within ChatGPT and beyond.

Topics such as prompt engineering, automation and orchestration, and language models are not limited to specific services or models. Because they can be applied to future models and services, this knowledge does not easily lose its value.

Requirements

It's not over yet

  • After taking this course, it is recommended to expand your knowledge by studying specific topics in depth, 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 significant amount 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 try prompt testing using tools such as OpenAI Platform, PromptLayer, and Promptfoo.

Recommended for
these people

Who is this course right for?

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

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

  • 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

Hello
This is pronist

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I am a former software backend developer and currently active as a freelance Generative AI software instructor. I am interested in the practical use of Generative AI, such as ChatGPT and prompt engineering. I provide lectures for practitioners such as aspiring entrepreneurs and planners. I help non-AI experts achieve great results by utilizing AI in startups and practical business operations.

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