Your question (translated):
What makes a good prompt, and how can it improve AI outputs?
First principles
A prompt is not "asking a question."
It is defining a function:
Output = f(Instruction, Context, Constraints, Format)
If output is bad, one of these inputs is underspecified.
What is a "good prompt"?
A good prompt = reduces ambiguity + increases structure
- Clear objective (What problem?)
Bad: "Explain AI"
Good: "Explain AI to a beginner in 3 key ideas with real-world examples"
👉 Principle: Minimize interpretation space
- Sufficient context (What background?)
AI has no memory of your situation unless you provide it.
Bad: "Give me diet advice"
Good: "28-year-old female, 54kg, wants simple healthy meals for weight maintenance"
👉 Principle: Context = constraints on solution space
- Explicit constraints (What limits?)
Without constraints → AI defaults to generic answers
Examples:
length: "in 100 words"
depth: "first-principles explanation"
audience: "for a non-technical person"
👉 Principle: Constraints force prioritization
- Output format (How should it look?)
Bad: open-ended
Good:
bullet points
table
step-by-step
JSON (for automation)
👉 Principle: Format = compress entropy into structure
- Role / perspective (Optional but powerful)
Example:
"Act as a senior product manager"
"Explain like a teacher using analogies"
👉 Principle: Role = bias the reasoning path
How this improves AI output
Think of AI as a probability engine over text.
A vague prompt → wide probability distribution → generic output
A precise prompt → narrow distribution → high-quality output
Example (before vs after)
❌ Weak prompt
"Help me invest"
→ Output: generic advice
✅ Strong prompt
"Act as a long-term value investor.
I have $10k, low risk tolerance, 5-year horizon.
Give 3 allocation strategies with pros/cons and expected risks."
→ Output:
structured
relevant
decision-ready
Advanced pattern (high leverage)
Use this template:
Goal: [what you want]
Context: [your situation]
Constraints:
-
limit 1
-
limit 2
Output format:
-
structure
Style:
-
optional
Meta insight
Good prompting is not about "better wording"
It is about:
Reducing uncertainty in the model's decision space
中文总结（提炼）
好的提示词本质是：
用目标 + 上下文 + 约束 + 格式，压缩AI的不确定性
核心三点：
减少歧义（说清你要什么）
增加约束（限制范围，避免泛化）
结构输出（让结果可用）
一句话：
👉 提示词不是"问问题"，而是"定义结果的边界条件"