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Off Topic Prompts

Ensures content stays within defined business scope using LLM analysis. Flags content that goes off-topic or outside your scope to help maintain focus and prevent scope creep.

Configuration

{
    "name": "Off Topic Prompts",
    "config": {
        "model": "gpt-5",
        "confidence_threshold": 0.7,
        "system_prompt_details": "Customer support for our e-commerce platform. Topics include order status, returns, shipping, and product questions.",
        "max_turns": 10
    }
}

Parameters

  • model (required): Model to use for analysis (e.g., "gpt-5")
  • confidence_threshold (required): Minimum confidence score to trigger tripwire (0.0 to 1.0)
  • system_prompt_details (required): Description of your business scope and acceptable topics
  • max_turns (optional): Maximum number of conversation turns to include for multi-turn analysis. Default: 10. Set to 1 for single-turn mode.
  • include_reasoning (optional): Whether to include reasoning/explanation fields in the guardrail output (default: false)
    • When false: The LLM only generates the essential fields (flagged and confidence), reducing token generation costs
    • When true: Additionally, returns detailed reasoning for its decisions
    • Performance: In our evaluations, disabling reasoning reduces median latency by 40% on average (ranging from 18% to 67% depending on model) while maintaining detection performance
    • Use Case: Keep disabled for production to minimize costs and latency; enable for development and debugging

Implementation Notes

  • LLM Required: Uses an LLM for analysis
  • Business Scope: system_prompt_details should clearly define your business scope and acceptable topics. Effective prompt engineering is essential for optimal LLM performance and accurate off-topic detection.

What It Returns

Returns a GuardrailResult with the following info dictionary:

{
    "guardrail_name": "Off Topic Prompts",
    "flagged": false,
    "confidence": 0.85,
    "threshold": 0.7,
    "token_usage": {
        "prompt_tokens": 1234,
        "completion_tokens": 56,
        "total_tokens": 1290
    }
}
  • flagged: Whether the content is off-topic (true = off-topic, false = on-topic)
  • confidence: Confidence score (0.0 to 1.0) for the assessment
  • threshold: The confidence threshold that was configured
  • token_usage: Token usage statistics from the LLM call
  • reason: Explanation of why the input was flagged (or not flagged) - only included when include_reasoning=true