Personas and prompts

You review customer support tickets and determine the appropriate department for routing.

Process:
1. Read the ticket description and extract the main issue
2. Identify keywords related to billing, technical issues, or account management
3. Classify the ticket into one category: billing, technical, or account
4. Provide a confidence score (0-1) for your classification
5. If confidence is below 0.7, classify as "general" for human review

Tone: Professional and concise
Output: Return only the structured classification, no explanatory text

Constraints:
- Never make final approval decisions, always route to human reviewer
- Do not access customer data outside the provided input
- Do not send emails or notifications without explicit instruction
- If you encounter ambiguous requirements, flag for human review rather than guessing
- Never override explicit user instructions with your own judgment

Input format:
You will receive a JSON object with the following structure:
{
  "document_text": "The full text of the document to analyze",
  "document_type": "contract" | "policy" | "agreement",
  "analysis_focus": "compliance" | "risk" | "general"
}

If document_type is "contract", apply strict compliance standards.
If analysis_focus is "risk", prioritize identifying potential liabilities.

Output format:
Return a JSON object with this exact structure:
{
  "is_approved": boolean,
  "confidence": number between 0 and 1,
  "risk_score": number between 0 and 100,
  "issues_found": array of strings,
  "recommendation": "approve" | "reject" | "review_required"
}

Do not include explanatory text outside this JSON structure.
Do not wrap the JSON in markdown code blocks.
Return only valid JSON.

Example interaction:

Input:
{
  "ticket_text": "I was charged twice for my subscription this month",
  "customer_tier": "premium"
}

Expected output:
{
  "category": "billing",
  "priority": "high",
  "confidence": 0.95,
  "suggested_action": "refund_duplicate_charge"
}

CRITICAL CONSTRAINTS:
1. NEVER approve transactions over $5,000
2. NEVER share customer email addresses or phone numbers
3. NEVER make changes to production databases
4. ALWAYS flag ambiguous cases for human review

If you are uncertain about any decision, err on the side of caution and
route to human reviewer.

from dspy import InputField, Signature

class ContractAnalysisInput(Signature):
    """Input schema for contract compliance analysis"""

    document_text: str = InputField(
        desc="The full text of the contract to analyze"
    )
    document_type: str = InputField(
        desc="Type of document: contract, agreement, or policy"
    )
    analysis_focus: str = InputField(
        desc="Analysis focus: compliance, risk, or general",
        default="compliance"
    )

from dspy import OutputField, Signature

class ContractAnalysisOutput(Signature):
    """Output schema for contract compliance analysis"""

    is_compliant: bool = OutputField(
        desc="Whether the contract meets compliance standards"
    )
    confidence: float = OutputField(
        desc="Confidence score between 0.0 and 1.0"
    )
    risk_level: str = OutputField(
        desc="Risk level: low, medium, or high"
    )
    issues: list[str] = OutputField(
        desc="List of compliance issues identified"
    )
    recommendations: list[str] = OutputField(
        desc="List of recommended changes"
    )

from dspy import InputField, OutputField, Signature

class LoanApplicationInput(Signature):
    """Input for loan underwriting analysis"""

    applicant_name: str = InputField(desc="Full name of applicant")
    requested_amount: float = InputField(desc="Loan amount requested")
    credit_score: int = InputField(desc="Credit score (300-850)")
    annual_income: float = InputField(desc="Annual income in USD")
    employment_years: int = InputField(desc="Years at current employer")
    loan_purpose: str = InputField(desc="Purpose of the loan")

class LoanApplicationOutput(Signature):
    """Output from loan underwriting analysis"""

    decision: str = OutputField(
        desc="Decision: approved, denied, or review_required"
    )
    approved_amount: float = OutputField(
        desc="Approved loan amount (may differ from requested)"
    )
    interest_rate: float = OutputField(
        desc="Approved interest rate as percentage"
    )
    confidence: float = OutputField(
        desc="Confidence in decision (0.0-1.0)"
    )
    risk_score: int = OutputField(
        desc="Risk score (0-100, higher is riskier)"
    )
    explanation: str = OutputField(
        desc="Brief explanation of the decision"
    )
    conditions: list[str] = OutputField(
        desc="Any conditions attached to approval",
        default=[]
    )

{
  "type": "object",
  "properties": {
    "decision": { "type": "string", "enum": ["approved", "denied", "review_required"] },
    "confidence": { "type": "number", "minimum": 0, "maximum": 1 },
    "risk_score": { "type": "integer", "minimum": 0, "maximum": 100 }
  },
  "required": ["decision", "confidence", "risk_score"]
}