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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.

(4.8) 30 reviews

130 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 lecture on generative AI for non-majors, targeting students and office workers. As it is a foundational 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 work and daily lives,' as well as the 'competency to critically evaluate AI.' It includes critical thinking to judge the reliability of information generated by generative AI and to consider ethical issues regarding AI usage.

AI Services: Do we need to learn them all?

The AI services currently on the market are 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 is often released.

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 are not something to be taken lightly. The term "digital rent" is even being used now. We need selection and focus, and it is necessary to concentrate on the representative AI services in each field.

First, we will focus on ChatGPT, the most representative generative AI service.

As ChatGPT is the most widely used generative AI service, it is the absolute basic of basics. Furthermore, in most cases, what can be done in ChatGPT can also be done in services like Gemini, Claude, and Grok, and vice versa.

Widely 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 vibe coding and multimodal capabilities that can process various types of data. Therefore, if you focus on and learn about the representative services, you will also be able to identify similar services offered by competitors.

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

The core of generative AI services is the "AI Agent".

When we make a request through a prompt, the AI agent within the service handles various tasks such as writing text, generating code, designing, and drawing pictures. Rather than focusing on flashy mastery of various tools, this course aims to help you master the "fundamentals," which are the common concepts used across various services, so that you can use agents more easily without being dependent on any specific service.

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 that cover individual topics like automation or vibe coding in depth, it was not easy to find a course that covers a broad and shallow range for an introduction to 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' generative AI, covering Prompt Engineering → Security → Automation → 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. However, rather than 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 slightly shallower level to utilize various AI services in practice.

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

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

Here's what you'll learn

  1. Prompt Engineering ― How to design questions so 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 ― Connecting AI with external services to configure automation workflows


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


Lecture Features

Practical-focused curriculum 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, and features a harmonious blend of theory and practice to help you master various use cases and technical concepts 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 topics such as GPT, BERT, Transformer, and Attention. Although diagrams are used for this purpose, complex code, mathematical concepts, or formulas are not included in the explanation.

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 and beyond ChatGPT.

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 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 perform prompt testing using tools such as OpenAI Platform, PromptLayer, and Promptfoo.

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

Hello
This is pronist

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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.

 

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  • Partner Instructor at Crowd Academy

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    This was very helpful!

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      It is a satisfying lecture.

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