Guide through creating an effective system prompt from scratch
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Guide the user through creating a high-quality system prompt using structured methodology and 2026 best practices.
Use AskUserQuestion to gather:
Question 1: "What type of application is this system prompt for?"
Question 2: "Describe the primary use case in one sentence" (Free text input)
Question 3: "What persona should the AI adopt?"
Question 4: "What tone should responses have?"
Question 5: "What are the key capabilities?" (multiSelect: true)
Question 6: "What should the AI refuse to do?" (multiSelect: true)
Question 7: "What output format is typically needed?"
Based on responses, generate using this template:
## System Prompt Template <identity> You are [persona description]. You [key traits and approach]. </identity> <capabilities> Your primary functions are: - [Capability 1] - [Capability 2] - [Capability 3] </capabilities> <guidelines> When responding: 1. [Guideline for tone/style] 2. [Guideline for accuracy] 3. [Guideline for format] </guidelines> <constraints> You must: - [Positive constraint 1] - [Positive constraint 2] When asked to [restricted action], politely explain that [reason] and offer [alternative]. </constraints> <output_format> Structure your responses as: [Format specification] </output_format> <examples> <example> <user>Example input</user> <assistant>Example output demonstrating desired behavior</assistant> </example> </examples>
Based on use case, add relevant sections:
<tools> You have access to the following tools: - tool_name: Description and when to use When using tools: 1. Explain your reasoning before tool calls 2. Handle errors gracefully 3. Verify results before presenting to user </tools> <tool_patterns> - For [situation], use [tool] with [approach] - Always [verification step] after [tool use] </tool_patterns>
<conversation_management> - Maintain context across turns - Reference previous discussion naturally - Ask clarifying questions when needed - Summarize long conversations when helpful </conversation_management> <state_tracking> Track these elements across the conversation: - [Element 1] - [Element 2] </state_tracking>
<code_practices> When writing code: 1. Follow language idioms and best practices 2. Include error handling 3. Add comments for complex logic 4. Prefer readability over cleverness 5. Test edge cases mentally before presenting </code_practices> <code_review> When reviewing code: 1. Check for security vulnerabilities 2. Identify performance issues 3. Suggest idiomatic improvements 4. Provide specific, actionable feedback </code_review>
<domain_knowledge> You are an expert in [domain] with deep knowledge of: - [Subdomain 1] - [Subdomain 2] Apply [framework/methodology] when analyzing problems. </domain_knowledge> <uncertainty_handling> When uncertain: - State your confidence level - Explain the source of uncertainty - Suggest how to verify information </uncertainty_handling>
Add Claude-specific optimizations:
<claude_4x_enhancements> <!-- For precise instruction following --> <default_to_action> By default, implement changes rather than only suggesting them. If intent is unclear, infer the most useful action and proceed. </default_to_action> <!-- For parallel efficiency --> <parallel_tools> When calling multiple independent tools, call them in parallel for efficiency. </parallel_tools> <!-- For long-horizon tasks --> <persistence> For complex tasks spanning multiple turns: - Track progress in structured notes - Save state before context limits - Resume gracefully from saved state </persistence> <!-- For avoiding over-engineering --> <simplicity> Avoid over-engineering. Only make changes directly requested. Keep solutions simple and focused. Don't add unnecessary abstractions or features. </simplicity> </claude_4x_enhancements>
## Testing Your System Prompt ### Test Cases to Run 1. **Happy path**: Standard use case request 2. **Edge case**: Unusual but valid request 3. **Boundary test**: Request at the edge of capabilities 4. **Refusal test**: Request that should be declined 5. **Ambiguity test**: Vague request to test clarification behavior ### Evaluation Criteria - Does it maintain the defined persona? - Are outputs in the expected format? - Does it handle refusals gracefully? - Is the tone consistent? - Does it use tools appropriately?