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

AI Literacy: Introduction to Generative AI for Non-Technical Professionals

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

Do I need to learn all the countless AI services?

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 particular AI service, another new service may emerge.

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. 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 services like 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. Not only general chatbots, but also specialized coding agents for vibe coding and multimodal capabilities that can process various types of data. Therefore, by focusing on representative services and learning about them, you'll be able to identify similar services from competitors as well.

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

• Generative AI ― ChatGPT, Claude Desktop

• Vibe Coding ― Cursor

• Automation ― ChatGPT Custom GPTs, ChatGPT Task, Make

• AI Agent ― AutoGen Studio

• Local AI ― LM Studio

• Browser Utility ― Sider

The core of generative AI services is AI agents. When we make requests through prompts, AI agents inside the service handle various tasks such as writing text, creating code, designing, and drawing pictures. This course doesn't focus on flashily using various tools, but rather helps you learn the fundamentals of agent components, creation, and usage, making it easier to use agents provided by various services.

What kind of lecture is this?

• "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 covering individual topics such as automation and vibe coding, courses that cover a broad and shallow range for an introduction to generative AI are not easy to find, so I decided to create one myself.

• This is a literacy education designed to be understandable even for non-experts, covering 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 explore the overall structure of language models.

• It focuses on AI "technology" literacy for 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 but somewhat shallow level, rather than a narrow and deep technical understanding of specific topics. It will open up new perspectives on utilizing generative AI.

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

• Service-centered AI courses generally have a short shelf life. Especially when new models are released or versions are updated, they often become obsolete. This course guards against knowledge becoming easily outdated by focusing not on specific services, but on mastering the fundamentals of generative AI.

You'll learn the following 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 ― Connect AI with external services to build automated workflows

   
   

5. Language Models ― Understanding the core components that make up language models, including learning, transformers, and attention

   
   

Course Features

It's not over yet

• After completing this course, it's recommended to expand your learning by studying deeper knowledge on specific topics such as LangChain, AI orchestration, and PromptOps.

• If you have an understanding of coding and development, it's also good to move forward with LangChain for LLM application development and fine-tuning to make language models domain experts. However, as 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 such as Make, Zapier, n8n, and Flowise.

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