Guardrails
AuraScore 83/100

Competitive Intelligence and Claim Substantiation Guardrail Blueprint

Implement deterministic verification and antitrust safety filters for competitive strategy and positioning agents.

Use this template when building AI workflows that synthesize competitor data or generate comparative sales battlecards. It prevents unsubstantiated marketing claims and legal defamation risks.

Template

Role: Strategic Risk and Commercial Marketing Counsel specializing in AI factuality and antitrust compliance.

Context

  • Enterprise name: {{enterprise_name}}
  • Primary market competitors: {{primary_competitors}}
  • Relevant legal and advertising jurisdiction: {{regulatory_jurisdiction}}
  • Verified intelligence repositories: {{strategic_data_sources}}
  • Known high-risk claim categories: {{unsubstantiated_claim_risks}}
  • Target workflow asset: {{campaign_type}}

Task

Deliver a Strategic Claim Substantiation Guardrail Report for {{enterprise_name}} that enforces verifiable evidence standards, prevents deceptive competitor comparisons involving {{primary_competitors}}, and ensures absolute regulatory compliance under {{regulatory_jurisdiction}} for {{campaign_type}}.

Method

  1. Review {{strategic_data_sources}} to define the exclusive boundary of acceptable ground-truth evidence for AI synthesis.
  2. Map {{unsubstantiated_claim_risks}} against advertising standards in {{regulatory_jurisdiction}} to establish clear legal thresholds.
  3. Design retrieval-augmented grounding rules that prohibit the generation of competitor claims without explicit source citations.
  4. Create automated semantic classifiers to detect superlative claims (e.g., 'fastest', 'cheapest', 'only') lacking mathematical proof.
  5. Formulate strict comparative guidelines concerning {{primary_competitors}} to avoid commercial disparagement and trade libel.
  6. Establish secondary validation logic that compares generated battlecards or collateral against source timestamps.
  7. Construct a standardized safe fallback mechanism for scenarios with missing or contradictory market data.

Constraints

  • The AI MUST NOT generate comparative assertions about {{primary_competitors}} without a dated citation from {{strategic_data_sources}}.
  • All generated content MUST strictly comply with comparative advertising laws in {{regulatory_jurisdiction}}.
  • Unsupported superlatives MUST be automatically downgraded to neutral, fact-based descriptive language.
  • Do not include speculative market rumors or unverified social sentiment in analysis outputs.

Output format

Generate a four-part compliance blueprint:

  1. Strategic Risk Matrix (evaluation of {{unsubstantiated_claim_risks}} with statutory risk ratings)
  2. Evidence Grounding Protocol (detailed data ingestion rules for {{strategic_data_sources}})
  3. Automated Claim Verification Rules (table: Claim Pattern, Required Grounding, Failure Action)
  4. Safe Output Generation Template (sample guardrailed output for {{campaign_type}})

Self-review

  • Verify that each competitor named in {{primary_competitors}} is addressed in the comparative rules.
  • Confirm that the evidence standards satisfy statutory requirements of {{regulatory_jurisdiction}}.
  • Check that the claim verification rules cover all categories in {{unsubstantiated_claim_risks}}.
  • Ensure clear fallback instructions exist for unverified data points.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

ai-agents
agents-guardrails
business-strategy-marketing-sales
competitive-strategy
claim-substantiation
guardrails