What Is Prompt Engineering? The Skill of Designing "How to Say It So AI Understands"
Prompt engineering is instruction design for getting generative AI (such as ChatGPT) to stably produce the output you want. It sounds difficult, but it is essentially close to "writing a good request document when you ask a person to do a job."
AI looks all-powerful, but it is actually worst at vague requests. Conversely, when purpose, premises, constraints, and evaluation criteria are all present, it works surprisingly smartly. In this article, we collect templates and techniques you can use starting tomorrow.
The Basic Structure to Master First: The "Set of 5" for a Good Prompt
For prompts, how well the information is assembled matters more than length. If you are unsure, try including the following set of 5.
- Purpose: What is it for? (e.g., improve CVR, write a spec, learning)
- Role: Who do you want the AI to be? (e.g., editor, SRE, recruiter)
- Input: What is the material? (e.g., meeting minutes, logs, text, requirements)
- Constraints: Conditions to obey (e.g., character count, tone, prohibitions)
- Output format: How do you want it returned? (e.g., table, bullets, JSON)
Template (OK to copy-paste)
Your role: Please act as an expert in XX.
Purpose: I want to achieve XX.
Background / premises: It is XX. The assumed reader/user is XX.
Input: Please use the following information: ...
Constraints: ... (prohibited / required / character count / tone)
Output format: In the order heading then bullets then conclusion. Use a Markdown table when a table is desirable.
A Collection of Prompt Techniques That Work (In Order of How Much They Help in Practice)
1) Include one "concrete example" (Few-shot)
AI suddenly stabilizes when you show an example rather than an abstract instruction. For instance, writing style and format align just by presenting one finished form.
Example: Please create 3 in this format.
[Example] Issue: ~ / Cause: ~ / Countermeasure: ~ / Expected effect: ~
2) First ask it to "ask questions before working"
When requirements are vague, the trick is not to let the AI run off. Having it ask clarifying questions first reduces rework.
