What are the prompt engineering features in Dify? How to write and optimize effective prompts?

Dify provides powerful prompt engineering features to help users optimize the output quality of AI applications. Core concepts include:

1. Prompt Template

   • Uses Jinja2 template syntax
   • Supports variable interpolation: {{variable}}
   • Supports conditional logic: {% if condition %}
   • Supports loops: {% for item in items %}

2. System Prompt

   • Defines AI's role and behavior
   • Sets output format requirements
   • Specifies response style and tone

3. Prompt Optimization Techniques

   • Clear and specific instructions: Use concrete, actionable instructions
   • Provide examples: Improve accuracy through few-shot learning
   • Step-by-step reasoning: Require AI to reason step by step
   • Constrain output format: Specify JSON, Markdown, etc.
   • Add context: Provide sufficient background information

4. Variable Management

   • Input variables: Dynamic data provided by users
   • Environment variables: Application-level configuration
   • Context variables: Conversation history and intermediate results

Example:

shell
You are a professional {{role}}, please answer the user's question based on the following information.

Background:
{{background}}

User question:
{{query}}

Requirements:
1. Answer should be accurate and professional
2. Use {{language}} language
3. Clear format, easy to read

Optimization suggestions:

• Use A/B testing to compare different prompts
• Continuously optimize based on user feedback
• Monitor model token usage
• Regularly update prompts to adapt to business changes

Candidates should understand the basic principles of prompt engineering and how to write and optimize effective prompts in Dify.