AgentPrompt Killer

Chaotic Description -> Structured Template

Input Area
Output Area
System Prompt
User Prompt




The Killer Diff - Enhancement Analysis
Role Definition Enhanced
Extract role information from input, strengthen professional identity positioning
Negative Constraints Strengthened
Automatically identify and enhance constraint conditions, avoid invalid outputs
Output Format Control
Standardize output format requirements, improve result consistency
Objective Clarified
Clarify task objectives, improve execution precision




FAQ - Prompt Engineering Q&A


What is System Prompt Optimization?
System Prompt Optimization is the process of structuring and optimizing system prompts to improve AI model performance. By clearly defining roles, task objectives, constraints, and output formats, the model can more accurately understand and execute tasks, significantly improving output quality and consistency. This is one of the core technologies in Prompt Engineering.


What are Claude 3.5 Sonnet Best Practices?
Claude 3.5 Sonnet supports 200K+ long context. Best practices include: 1) Clear role definition (e.g., You are a senior software architect); 2) Explicit task description; 3) Detailed constraint specifications; 4) Expected output format requirements; 5) Using structured frameworks like CO-STAR or Professional Role. These practices can significantly improve Claude 3.5 Sonnet output quality.


What is the CO-STAR Framework?
The CO-STAR Framework is a professional structured prompting method containing six core elements: Context (background information), Objective (clear task goal), Style (output style requirements), Tone (language tone setting), Audience (target audience), and Response (expected output format). This is one of the most commonly used frameworks in System Prompt Optimization.


How to create effective AI Prompts?
Effective AI Prompts should include: clear role definition, specific task description, clear rule constraints, and expected output format. Using structured frameworks like CO-STAR or Professional Role can significantly improve prompt effectiveness, while combining negative constraints and output format control can further optimize results. This is the core principle of Prompt Engineering Methodology.


What is Prompt Engineering Methodology?
Prompt Engineering Methodology is a systematic approach to prompt design, including: text preprocessing, keyword recognition, feature extraction, template filling, and output optimization. It uses regular expressions to parse natural language, automatically identifying key elements like roles, tasks, and constraints, while auto-completing missing structured information. This is the core technology used by AgentPrompt Killer.




Prompt Engineering Methodology


Text Preprocessing Stage
Input text first goes through preprocessing: removing extra whitespace, unifying list formats (dashes, numbered lists), and cleaning special characters.
Key processing:
  • Use regex /\s+/g to merge consecutive whitespace
  • Use regex /^(\s*[-•*]\s*)+/gm to unify unordered list format
  • Use regex /^(\s*\d+[\.\)]\s*)+/gm to unify ordered list format


Keyword Recognition & Role Extraction
Identify keywords through regular expressions to extract role information:
  • Role indicators: /^(You are|As a|Act as|I need you|Please)/i
  • Expert indicators: /^(Expert|Professional|Senior|Advanced)/i
Example: Input "You are a senior software engineer" will automatically extract role as "As a professional software engineer".


Objective Extraction
Identify content after action verbs as task objectives:
  • Task verbs: /^(Do|Write|Generate|Develop|Create|Design|Analyze|Optimize|Organize|Translate)/i
Example: Input "Write an article about AI" will extract objective as "Write an article about AI".


Constraints Extraction
Identify negative words as constraint conditions:
  • Constraint indicators: /^(Do not|Never|Must not|Avoid|Cannot|Should not)/i
  • Rule indicators: /^(Rule|Requirement|Condition)/i
Example: Input "Do not use complex terminology" will extract constraint as "Do not use complex terminology".


Context Extraction
Identify background information as context:
  • Context indicators: /^(Based on|According to|Given|Background)/i
Example: Input "Based on user provided data" will extract context as "Based on user provided data".


Missing Value Completion & Template Filling
For features not extracted, the system automatically fills in generic enhancement descriptions:
  • Style: Professional, Concise, and Logic-driven
  • Tone: Formal, helpful, and informative
  • Audience: General audience seeking professional guidance
  • Workflow: Analyze requirements -> Plan solution -> Execute task -> Deliver results
Then all extracted features are filled into the selected framework template (CO-STAR, Professional Role, or Simple Structuring).