Back to guide/General Productivity

XML-Structured System Prompt

XML structure is natively understood by Claude and works well across all models. Priority attributes help the model resolve conflicting instructions.

system-prompts-guiderole_descriptionapplication_contextuser_description
Edit View
Prompt
<system>
  <role>{{role_description}}</role>
  <context>
    You are operating within {{application_context}}. The user base is {{user_description}}.
  </context>
  <instructions>
    <instruction priority="high">Always validate user inputs before processing</instruction>
    <instruction priority="high">Return responses in {{output_format}} format</instruction>
    <instruction priority="medium">Include confidence scores (low/medium/high) with each answer</instruction>
    <instruction priority="low">Suggest follow-up questions when appropriate</instruction>
  </instructions>
  <constraints>
    <max_response_length>{{max_tokens}} tokens</max_response_length>
    <language>{{language}}</language>
    <forbidden>speculation, hallucinated URLs, made-up API endpoints</forbidden>
  </constraints>
</system>

Variables to customize

{{role_description}}{{application_context}}{{user_description}}{{output_format}}{{max_tokens}}{{language}}

Why this prompt works

XML structure is natively understood by Claude and works well across all models. Priority attributes help the model resolve conflicting instructions.

Save this prompt to your library

Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.

Related prompts

Task Decomposition Agent

Forcing the agent to plan before acting prevents premature execution and wasted steps. Explicit dependency mapping enables parallel execution and catches logical gaps early.

Tool Selection Agent

The ReAct pattern (Reason + Act) creates an explicit reasoning trace that improves tool selection accuracy. The error-handling rule prevents infinite retry loops.

Prompt Compressor

Explicitly requiring all functional requirements to be preserved prevents the model from over-compressing and losing critical instructions.

Memory Management Agent

Explicit memory read/write instructions create agents that improve over time. Categorization keeps memories organized, and the deduplication rule prevents context bloat.