On this page, we have organized the introductory knowledge, technical explanations, use cases, risks, and prompt techniques you need to really make good use of generative AI, grouped by theme so you can see everything at a glance.
Even if you are not an engineer, or you are just starting to use generative AI for work or study, the articles are arranged so that by reading them in order you can naturally build the AI literacy that forms your foundation.
If you are new here, we recommend starting with the “Introduction and big picture” section.
By first understanding the basic ideas behind generative AI and how it differs from traditional AI, it becomes easier to follow the later technical articles and the ones on practical use and risks as one continuous story.
1. Intro & Overview｜First grasp “What is generative AI?”
These articles help you get a rough overview of generative AI: its basic concepts, social impact, and how it differs from traditional AI. Recommended for your first pass.
- What is the social impact of generative AI? — This article organizes the impact, often said to be greater than that of smartphones, from the perspectives of education, industry, government, and everyday life.
- Understanding the differences between traditional AI and generative AI — Explains classic comparison points such as how it differs from task-specific AI and how input/output formats differ.
2. Models & Tech｜Learn the mechanisms and reduce the “black box” feeling
These more technical articles dive inside generative AI to help you understand LLMs, foundation models, transformers, learning methods, hallucinations, and more.
- What technical features do generative AI models share? — Organizes the common technical foundations shared by various generative AIs such as text and image models.
- Basic structure and training methods of large language models (LLMs) — Gently explains the structure and training flow of LLMs while avoiding heavy mathematics as much as possible.
- Supervised learning, self-supervised learning, pre-training, and fine-tuning explained in simple terms — Uses these four keywords to organize the learning process of generative AI.
- What are foundation models, transformers, and attention? — Explains, with illustrative images, the architectures that form the heart of modern generative AI.
- What are probabilistic models and hallucinations? Understanding the “thinking habits” of generative AI — Explains why hallucinations occur, based on the mechanism of “choosing the next token by probability.”
- What are alignment and instruction tuning? — Covers the basics of techniques that adjust models to behave in ways that are more useful and appropriate for humans.
3. Use Cases｜Concrete ideas of “where and how to use it”
These articles help you imagine “how it is actually being used” in areas such as education, business, government, development, and language learning.
- How is generative AI being used? Understanding current utilization through concrete examples — Organizes representative use cases by field such as education, business, government, development, and everyday life.
- What are the limiting factors for using generative AI? — Explains why adoption does not progress as quickly as expected, from angles such as technology, law, ethics, social acceptance, and cost.
- Learning English with generative AI for beginners! A simple three-step method — Introduces concrete prompt examples for using generative AI in English learning through three steps: vocabulary building → correction → conversation practice.
4. Risks & Law｜Essential knowledge for safe use
These articles cover hallucinations, bias, information leaks, legal regulations, and other topics you should know in order to use generative AI “safely” and “responsibly.”
- What risks does generative AI pose and how can users reduce them? Five basic countermeasures explained — A hands-on article that summarizes typical mitigation steps that ordinary users can take.
- What are input risks for generative AI? Precautions and countermeasures for safe use — Focuses on what you should not enter, such as personal data and confidential information.
- What are the legal, ethical, and security risks of generative AI? Key points explained with concrete examples — A base article that helps you grasp law, ethics, and security in a cross-cutting way.
5. Prompts & Flows｜Reusable patterns for day-to-day work
These articles focus on prompt patterns and workflows so that AI literacy does not remain just “knowledge,” but is actually used in your daily work and study.
- Ten basic prompt patterns: a set of “minimum templates” you can use every day — Organizes ten highly reusable patterns such as role assignment, constraint specification, step-by-step thinking, few-shot prompting, summarization/extraction/transformation, and evaluation, together with ready-to-use templates.