Introduction: AI Terms Get Much Easier Once You Cross the "Katakana-English Wall"
If you follow generative AI topics, technical terms keep appearing one after another—prompt, RAG, fine-tuning, agent... They are hard to approach at first, but in fact they are all words of role division that express "how you have AI work for you."
In this article, we organize the important words frequently used in the field as accessibly as possible, in the order "in a word," "when do you use it?", "merits/cautions," and "how to choose."
Prompt: A "Request Message" to AI
In a word
A prompt is the instruction text that tells generative AI "what you want done and how." It is the input for the AI and an important point that determines the quality of the deliverable.
When do you use it?
- Writing (emails, proposals, articles, summaries)
- Code generation (creating functions, fixing bugs, review)
- Image generation (composition/style specification)
- Data organization (formatting tables, classification, extraction)
Tip: prompts get stronger with "role," "constraints," and "examples"
The three classic templates that work well are:
- Role: please answer as an expert in XX
- Constraints: character count, tone, output format (JSON/table/bullets)
- Examples: good examples, bad examples, an expected output sample
Example: "You are a PM for BtoB SaaS. Produce 3 new-feature ideas, each with aim, target user, and KPI, in bullets. Briefly supplement technical terms."
Cautions
- Vague instructions tend to make answers vague too (the "make it nice" problem)
- It diverges with insufficient shared premises (target, purpose, prohibitions)
- Also be careful about information management, such as not including confidential information
RAG (Retrieval-Augmented Generation): A Mechanism to Add "Basis" Through Search
In a word
RAG is a mechanism where, before the AI builds an answer, it searches related information from internal documents or a database (Retrieval) and then generates (Generation) based on that content. Roughly, picture "letting the AI crib from internal knowledge."
When do you use it?
- Chatbots using internal rules, procedure manuals, FAQs, and minutes
- Support answers while referring to product manuals
- Drafting sales materials and proposals (referring to past cases)
- First-line legal/compliance checks (with internal-rule citations)
Merits
- Can reflect the latest information and company-specific information (OK even if the model hasn't trained on it)
- Easier to reduce hallucination (plausible errors)
