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

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 liberal arts course on generative AI for non-majors, targeting students and working professionals.

AI services... Do I need to learn them all?

AI services are currently like a battlefield. Various AI services are striving to become the 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, another new service may emerge. It's not easy to thoroughly use and compare all AI services even within the same field. Moreover, with so many services available, the subscription costs incurred cannot be taken lightly. We need selective focus and need to concentrate on representative AI services in each field.

We first focus on ChatGPT, the most representative generative AI service. In this course, we have included content that allows you to naturally use these services while learning related concepts, excluding specialized fields that require domain knowledge such as design, video, music, and comics. The AI services used in this course are composed of services with low entry barriers and difficulty levels that even non-experts can sufficiently use.

• 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 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, and drawing images. In this course, rather than focusing on flashy use of various tools, we aim to learn the fundamental components of agents and the basics for creating and using them, enabling quick adoption of agents provided by various services, and by mastering the fundamentals, we guard against knowledge quickly becoming obsolete.

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 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 practical 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.

• This course covers most of the generative AI learning roadmaps: AI Agents Roadmap and Prompt Engineering Roadmap. However, while this course is sufficient as a starting point for generative AI learning, it cannot be the end of it.

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 the core components that make up language models, including learning, transformers, and attention

   
   

Course Features

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.