Effective Prompt Writing Techniques for Human‑AI Collaboration

Prompt engineering bridges human and AI interaction, demanding cognitive decomposition, instruction engineering, and model alignment. Effective prompt design requires systematic thinking to translate implicit needs into structured commands.

Skill Requirements

1. Logical Decomposition – Apply frameworks such as CRISPE or COAST to break vague needs into Goal, Role, Context, Constraint, Output Format, and Examples. A positive example asks the model to assume a specific role in a defined context; a negative example simply says “make a good plan”.

2. Language Precision – Use imperative sentences, clear verbs, and quantitative descriptions; avoid ambiguous terms like “maybe” or “perhaps”.

3. Scenario Adaptation – Understand characteristics of different models (e.g., GPT, Claude, Gemini) and tool‑specific prompt handling; adjust instruction detail accordingly.

4. Iterative Optimization – Introduce a scientific iteration loop: evaluate output, perform error attribution (logic, factual, or format errors), adjust the specific prompt part, and optionally run A/B tests of different prompt versions to measure improvement.

5. Ethics and Safety – Anticipate bias, misinformation, or harmful content risks; design safety guardrails to keep model outputs ethical and compliant with regulations.

Prompt Symbol Conventions

Basic Formatting Symbols – Use # for headings, - or * for lists, and ** for bold text to improve readability and structure.

Functional Symbols – [] denotes optional parameters, ` separates multiple options, === creates section dividers, and // adds comments that do not affect execution.

Prohibited / Cautious Use – Avoid excessive special symbols (e.g., @#￥%) that may cause parsing errors; follow official tool documentation; never use offensive symbols.

Other Conventions – Consistently use Markdown syntax; employ separators like --- or ***; embed structured tags such as <user_input> or <knowledge_base> to encapsulate variables and assist model parsing.

Common Prompt Templates

1. General Basic Template

# Role: [specific role, e.g., marketing planner, Python developer]
## Profile
- Professional background: [...]
- Communication style: [...]
## Skills
- Core ability 1: [...]
- Core ability 2: [...]
## Rules
1. Follow constraints [...]
2. Reject prohibited content [...]
3. Prioritize feasibility [...]
## Initialization
Based on user input, confirm core requirements, then deliver results in the specified output format, asking at most two clarification questions if needed.

2. Advanced Enhanced Template

# Supplementary Knowledge
<markdown_knowledge>
1. Domain rules: [...]
2. Reference data: [...]
3. Tool limits: [...]
</markdown_knowledge>
# Task: [task title]
## Requirement Description
- Core goal: [...]
- Application scenario: [...]
- Special requirements: [...]
## Principles
1. Feasibility first
2. Data‑backed decisions
3. Compliance bottom line
## Workflows
1. Need decomposition: [...] 
2. Strategy design: [...] 
3. Resource allocation: [...] 
4. Risk prediction: [...] 
## OutputFormat
1. Type: Markdown document
2. Structure: [...] 
## User Input
<user_input>
[User's raw request]
</user_input>
## Evaluation Criteria
1. Completeness: [...] 
2. Feasibility: [...] 
3. Accuracy: [...]

3. Tool‑Specific Template (Programming Example)

# Role: Senior Python developer (data processing)
## Tool Constraints
- Use Python 3.10+ libraries, prefer pandas, numpy
- No paid third‑party libraries; compatible Windows/Linux
## Task Requirements
- Goal: Clean CSV and generate visualization
- Input: CSV with user ID, amount, timestamp
- Output: Cleaned CSV + line chart (PNG)
## Code Standards
1. Snake_case naming, clear function names
2. Comments and docstrings for key logic
3. Error handling with exceptions
4. Performance: ≤30 s for 100k rows
## OutputFormat
1. Code block: ```python ... ```
2. Execution instructions
3. Result verification steps

Professional Prompt Tuning Tools

Volcengine PromptPilot – Chinese prompt generation, optimization, and template library (https://promptpilot.volcengine.com/home)

Open‑source Prompt Optimizer – Free formatting and standardization, customizable, self‑hostable (https://prompt.always200.com/)

OpenRouter.ai – Unified multi‑model calling and prompt testing, supports 300+ models (https://openrouter.ai/)

Awesome AI System Prompts – Curated template collection covering 20+ tools (https://github.com/dontriskit/awesome-ai-system-prompts)

Perplexity AI – Search‑based prompt optimization and fact‑checking (https://perplexity.ai/)

FAQ and Troubleshooting

Vague requirements – Decompose using the five‑dimensional framework and add missing details.

Information overload – Trim redundant parts, keep core instructions only.

Messy formatting – Use headings, lists, and bold text to highlight key points.

Tool incompatibility – Consult the tool’s documentation and adapt syntax accordingly.

Lack of evaluation criteria – Define quantitative metrics or validation methods.

Learning Resources

Official model Prompt guides (OpenAI, Anthropic, Google Gemini).

Community repositories and Prompt Engineering topics on GitHub and Zhihu.

Case libraries such as Awesome AI System Prompts.

Advanced books: “Prompt Engineering for AI”, “AI Prompt Mastery”.