Guidelines-based LLM scorers

Overview

Guidelines-based judges and scorers use pass/fail natural language criteria to evaluate GenAI outputs. They excel at evaluating:

• Compliance: "Must not include pricing information"
• Style/tone: "Maintain professional, empathetic tone"
• Requirements: "Must include specific disclaimers"
• Accuracy: "Use only facts from provided context"

Benefits

• Business-friendly: Domain experts write criteria without coding
• Flexible: Update criteria without code changes
• Interpretable: Clear pass/fail conditions
• Fast iteration: Rapidly test new criteria

Three ways to use guidelines

MLflow provides three approaches for using guidelines-based judges:

1. Prebuilt Guidelines() scorer: Apply global guidelines uniformly to all rows. Evaluates app inputs/outputs only. Works in both offline evaluation and production monitoring.
2. Prebuilt ExpectationsGuidelines() scorer: Apply per-row guidelines labled by ___domain experts in an evaluation dataset. Evaluates app inputs/outputs only. For offline evaluation only.
3. judges.meets_guidelines() SDK: Apply guidelines to any trace data (tool calls, retrieved context, etc). Must be wrapped in a custom scorer to use for evaluation/monitoring.

How guidelines work

Guidelines-based judges use a specially-tuned LLM to evaluate whether text meets your specified criteria. The judge:

1. Receives context: Any JSON dictionary containing the data to evaluate (e.g., request, response, retrieved_documents, user_preferences). You can reference these keys by name directly in your guidelines - see detailed examples
2. Applies guidelines: Your natural language rules defining pass/fail conditions
3. Makes judgment: Returns a binary pass/fail score with detailed rationale

Learn more about the models powering the LLM judges.

Prerequisites for running the examples

1. Install MLflow and required packages

   pip install --upgrade "mlflow[databricks]>=3.1.0"
   

2. Create an MLflow experiment by following the setup your environment quickstart.

1. Prebuilt Guidelines() scorer: global guidelines

The Guidelines scorer applies uniform guidelines across all rows in your evaluation or traces in production monitoring. It automatically extracts request/response data from your trace and evaluates it against your guidelines.

When to use

Use the prebuilt scorer when:

• Your guidelines only need the app's inputs and outputs
• Your trace has standard input/output structure
• You want quick setup without custom code

Examples

In your guideline, refer to the app's inputs as the request and the app's outputs as the response.

from mlflow.genai.scorers import Guidelines
import mlflow

# Example data
data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": {"response": "The capital of France is Paris."}
    },
    {
        "inputs": {"question": "What is the capital of Germany?"},
        "outputs": {"response": "The capital of Germany is Berlin."}
    }
]

# Create scorers with global guidelines
english = Guidelines(
    name="english",
    guidelines=["The response must be in English"]
)

clarity = Guidelines(
    name="clarity",
    guidelines=["The response must be clear, coherent, and concise"]
)

# Evaluate with global guidelines
results = mlflow.genai.evaluate(
    data=data,
    scorers=[english, clarity]
)

from mlflow.genai.scorers import Guidelines
import mlflow


# Create evaluation dataset with pre-computed outputs
eval_dataset = [
    {
        "inputs": {
            "messages": [{"role": "user", "content": "My order hasn't arrived yet"}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "I understand your concern about the delayed order. Let me help you track it right away."
                }
            }]
        },
    },
    {
        "inputs": {
            "messages": [{"role": "user", "content": "How do I reset my password?"}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "To reset your password, click 'Forgot Password' on the login page. You'll receive an email within 5 minutes."
                }
            }]
        },
    }
]

# Run evaluation on existing outputs
results = mlflow.genai.evaluate(
    data=eval_dataset,
    scorers=[Guidelines(name="tone", guidelines="The response must maintain a courteous, respectful tone throughout. It must show empathy for customer concerns in the request"),]
)

Parameters

Advanced example with tone and accuracy

How the prebuilt scorer parses your app's inputs/oututs

The scorer automatically extracts data from your trace to create the guideline's context with the keys request and response:

Request

The request field is extracted from your inputs.

• If your inputs contains a messages key with an array of OpenAI format chat messages then:
  • If there is a single message → request is that message's content
  • If there are 2+ messages → request is the entire messages array serialized to a JSON string.
• Otherwise, request is the entire inputs dict serialized to a JSON string.

Examples

Single message input:

# Input
inputs = {
    "messages": [
        {"role": "user", "content": "How can I reset my password?"}
    ]
}

# Parsed request
"How can I reset my password?"

Multi-turn conversation:

# Input
inputs = {
    "messages": [
        {"role": "user", "content": "What is MLflow?"},
        {"role": "assistant", "content": "MLflow is an open source platform..."},
        {"role": "user", "content": "Tell me more about tracing"}
    ]
}

# Parsed request (JSON string)
'[{"role": "user", "content": "What is MLflow?"}, {"role": "assistant", "content": "MLflow is an open source platform..."}, {"role": "user", "content": "Tell me more about tracing"}]'

Arbitrary dict:

# Input
inputs = {"key1": "Explain MLflow evaluation", "key2": "something else"}

# Parsed request
'{"key1": "Explain MLflow evaluation", "key2": "something else"}'

Response

The response field is extracted from your outputs:

• If your outputs contains an OpenAI format ChatCompletions object:
  • response is the first choice's content
• If your outputs contains a messages key with an array of OpenAI format chat messages
  • response is the last message's content
• Otherwise, response is the outputs serialized to a JSON string.

Examples

ChatCompletion output:

# Output (simplified)
outputs = {
    "choices": [{
        "message": {
            "content": "MLflow evaluation helps measure GenAI quality..."
        }
    }]
}

# Parsed response
"MLflow evaluation helps measure GenAI quality..."

Messages format output:

# Output
outputs = {
    "messages": [
        {"role": "user", "content": "What are the ..."}
        {"role": "assistant", "content": "Here are the key features..."}
    ]
}

# Parsed response
"Here are the key features..."

Arbitrary dict:

# Input
inputs = {"key1": "Explain MLflow evaluation", "key2": "something else"}

# Parsed request
'{"key1": "Explain MLflow evaluation", "key2": "something else"}'

2. Prebuilt ExpectationsGuidelines() scorer: per-row guidelines

The ExpectationsGuidelines scorer evaluates against row-specific guidelines from ___domain experts. This allows different evaluation criteria for each example in your dataset.

When to use

Use this scorer when:

• You have ___domain experts who have labeled specific examples with custom guidelines
• Different rows require different evaluation criteria

Example

In your guideline, refer to the app's inputs as the request and the app's outputs as the response.

from mlflow.genai.scorers import ExpectationsGuidelines
import mlflow

# Dataset with per-row guidelines
data = [
    {
        "inputs": {"question": "What is the capital of France?"},
        "outputs": "The capital of France is Paris.",
        "expectations": {
            "guidelines": ["The response must be factual and concise"]
        }
    },
    {
        "inputs": {"question": "How to learn Python?"},
        "outputs": "You can read a book or take a course.",
        "expectations": {
            "guidelines": ["The response must be helpful and encouraging"]
        }
    }
]

# Evaluate with per-row guidelines
results = mlflow.genai.evaluate(
    data=data,
    scorers=[ExpectationsGuidelines()]
)

Parameters

No parameters required. Automatically reads guidelines from the expectations/guidelines field in your dataset. All guidelines for a single row are evaluated together as one call to the underlying judge.

How the prebuilt scorer parses your app's inputs/oututs

The parsing is identical to the parsing for the Guidelines() scorer.

3. judges.meets_guidelines() SDK

For complex evaluation scenarios, use the judges.meets_guidelines() API directly within custom scorers. This provides maximum flexibility for evaluating any aspect of your GenAI application.

When to use

Use the API when you need to:

• Evaluate data beyond inputs/outputs (retrieved documents, tool calls, metadata)
• Exclude certain fields from evaluation (user IDs, timestamps)
• Apply different guidelines to different parts of your data
• Combine multiple guideline evaluations with custom logic

Example

from mlflow.genai.judges import meets_guidelines

feedback = meets_guidelines(
    name="factual_accuracy",
    guidelines="The response must only use facts from retrieved_documents. It is OK if the response references concepts from the request even if those concepts are not in the retrieved_documents.",
    context={
        "request": "What products did the customer Acme Co purchase?",
        "response": "Acme Co purchased laptop and mice",
        "retrieved_documents": ["laptop", "mouse"]
    }
)

print(feedback.value)
print(feedback.rationle)

Parameters

Custom scorer example

from mlflow.genai.scorers import scorer
from mlflow.genai.judges import meets_guidelines
import mlflow

@scorer
def tone_and_accuracy(inputs, outputs):
    """Custom scorer that evaluates tone and accuracy using guidelines"""
    # Note: In production, you'd extract retrieved_documents from the trace.
    # For this example, we're passing it directly in outputs for simplicity.
    retrieved_docs = outputs.get("retrieved_documents", [])

    # Evaluate multiple aspects
    feedbacks = []

    # Check tone
    tone_feedback = meets_guidelines(
        name="professional_tone",
        guidelines="The response must maintain a professional, helpful tone",
        context={
            "request": inputs.get("question"),
            "response": outputs.get("answer")
        }
    )
    feedbacks.append(tone_feedback)

    # Check accuracy against retrieved documents
    if retrieved_docs:
        accuracy_feedback = meets_guidelines(
            name="factual_accuracy",
            guidelines="The response must only use facts from retrieved_documents",
            context={
                "request": inputs.get("question"),
                "response": outputs.get("answer"),
                "retrieved_documents": retrieved_docs
            }
        )
        feedbacks.append(accuracy_feedback)

    return feedbacks

# Example evaluation dataset
eval_data = [
    {
        "inputs": {
            "question": "What is MLflow's primary purpose?"
        },
        "outputs": {
            "answer": "MLflow is an open-source platform designed to manage the ML lifecycle, including experimentation, reproducibility, and deployment.",
            "retrieved_documents": [
                "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle.",
                "It provides tools for tracking experiments, packaging code, and deploying models."
            ]
        }
    }
]

# Run evaluation with the custom scorer
results = mlflow.genai.evaluate(
    data=eval_data,
    scorers=[tone_and_accuracy]
)

Return values

All approaches return an mlflow.entities.Feedback object containing:

• value: Either "yes" (meets guidelines) or "no" (fails guidelines)
• rationale: Detailed explanation of why the content passed or failed
• name: The assessment name (either provided or auto-generated)
• error: Error details if evaluation failed

Writing effective guidelines

Well-written guidelines are crucial for accurate evaluation. Follow these best practices:

Reference context variables

Include any key from your context dictionary directly in guidelines:

# Example 1: Validate against retrieved documents
context = {
    "request": "What is the refund policy?",
    "response": "You can return items within 30 days for a full refund.",
    "retrieved_documents": ["Policy: Returns accepted within 30 days", "Policy: No refunds after 30 days"]
}
guideline = "The response must only include information from retrieved_documents"

# Example 2: Check user preferences
context = {
    "request": "Recommend a restaurant",
    "response": "I suggest trying the new steakhouse downtown",
    "user_preferences": {"dietary_restrictions": "vegetarian", "cuisine": "Italian"}
}
guideline = "The response must respect user_preferences when making recommendations"

# Example 3: Enforce business rules
context = {
    "request": "Can you apply a discount?",
    "response": "I've applied a 15% discount to your order",
    "max_allowed_discount": 10,
    "user_tier": "silver"
}
guideline = "The response must not exceed max_allowed_discount for the user_tier"

# Example 4: Multiple constraints
context = {
    "request": "Tell me about product features",
    "response": "This product includes features A, B, and C",
    "approved_features": ["A", "B", "C", "D"],
    "deprecated_features": ["X", "Y", "Z"]
}
guideline = """The response must:
- Only mention approved_features
- Not include deprecated_features"""

Best practices

Be specific and measurable
✅ "The response must not include specific pricing amounts or percentages"
❌ "Don't talk about money"

Use clear pass/fail conditions
✅ "If asked about pricing, the response must direct users to the pricing page"
❌ "Handle pricing questions appropriately"

Reference context explicitly
✅ "The response must only use facts present in retrieved_context"
❌ "Be factual"

Structure complex requirements

guideline = """The response must:
- Include a greeting if first message
- Address the user's specific question
- End with an offer to help further
- Not exceed 150 words"""

Real world examples

Customer service chatbot

Here are practical guidelines examples for evaluating a customer service chatbot across different scenarios:

Global guidelines for all interactions

from mlflow.genai.scorers import Guidelines
import mlflow

# Define global standards for all customer interactions
tone_guidelines = Guidelines(
    name="customer_service_tone",
    guidelines="""The response must maintain our brand voice which is:
    - Professional yet warm and conversational (avoid corporate jargon)
    - Empathetic, acknowledging emotional context before jumping to solutions
    - Proactive in offering help without being pushy

    Specifically:
    - If the customer expresses frustration, anger, or disappointment, the first sentence must acknowledge their emotion
    - The response must use "I" statements to take ownership (e.g., "I understand" not "We understand")
    - The response must avoid phrases that minimize concerns like "simply", "just", or "obviously"
    - The response must end with a specific next step or open-ended offer to help, not generic closings"""
)

compliance_guidelines = Guidelines(
    name="policy_compliance",
    guidelines=[
        """Refund and return policies:
        - The response must not promise refunds beyond the 30-day return window
        - The response must mention restocking fees for electronics (15%) if discussing returns
        - For items over $500, the response must note that manager approval is required
        - The response must not waive shipping fees without checking customer loyalty tier""",

        """Data privacy and security:
        - The response must never ask for full credit card numbers, SSN, or passwords
        - The response must not reference other customers' orders or information
        - When discussing order details, the response must only reference the last 4 digits of payment methods
        - The response must direct customers to secure portal for updating payment information""",

        """Commitment limitations:
        - The response must not guarantee specific delivery dates without system verification
        - The response must include disclaimer about "estimated" times when discussing shipping
        - The response must not promise price matches without mentioning the verification process
        - For out-of-stock items, the response must not commit to restock dates"""
    ]
)

# Example customer service data
customer_interactions = [
    {
        "inputs": {
            "messages": [{"role": "user", "content": "I've been waiting 2 hours on hold! This is ridiculous!"}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "I completely understand your frustration about the long wait time. That's absolutely not the experience we want you to have. I'm here to help you right now - what can I assist you with today?"
                }
            }]
        }
    },
    {
        "inputs": {
            "messages": [{"role": "user", "content": "Can I return my laptop? I bought it 45 days ago."}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "I'd be happy to look into return options for you. Since our standard return window is 30 days and your purchase was 45 days ago, I'll need to check if there are any exceptions we can make. For electronics like laptops, we do have a 15% restocking fee. May I have your order number to review your specific situation?"
                }
            }]
        }
    }
]

# Evaluate customer service interactions
results = mlflow.genai.evaluate(
    data=customer_interactions,
    scorers=[tone_guidelines, compliance_guidelines]
)

Per-row guidelines for specific scenarios

from mlflow.genai.scorers import ExpectationsGuidelines
import mlflow

# Dataset with scenario-specific guidelines
customer_service_data = [
    {
        "inputs": {
            "messages": [{"role": "user", "content": "My package is 3 weeks late and I need it for an event tomorrow!"}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "I can absolutely understand how stressful this must be, especially with your event tomorrow. I'm so sorry your package has been delayed for 3 weeks - that's completely unacceptable. Let me help you immediately. I'm checking our local store inventory right now for same-day pickup options. I'm also processing a full refund of your shipping costs and applying a 25% discount to your order for this inconvenience. Would you prefer overnight delivery to arrive by 10 AM tomorrow, or would same-day pickup from our downtown ___location work better for you? I'm also connecting you with our escalation team - you'll receive a call within the next hour at the number on your account."
                }
            }]
        },
        "expectations": {
            "guidelines": [
                """The response must handle this urgent situation with exceptional care:
                - First acknowledge both the delay AND the urgent tomorrow deadline
                - Express genuine empathy using phrases like "I can absolutely understand how stressful this must be"
                - Must NOT make excuses or blame shipping partners""",

                """The response must provide immediate actionable solutions:
                - Offer to check local store availability for same-day pickup
                - If available, offer overnight shipping at no charge given the delay
                - Provide direct escalation path: manager callback within 2 hours
                - Include the executive customer service email for formal complaints""",

                """The response must include compensation options without being asked:
                - Mention automatic refund of original shipping costs
                - Offer 20-30% discount on current order due to inconvenience
                - Provide credit for future purchase if customer prefers"""
            ]
        }
    },
    {
        "inputs": {
            "messages": [{"role": "user", "content": "How do I cancel my subscription?"}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "I can help you cancel your subscription right away. Here's how: 1) Click 'Account Settings' in the top right corner, 2) Select 'Subscription' from the menu, 3) Click 'Cancel Subscription' and confirm. Your access will continue until March 15, 2024 (the end of your current billing period), and you won't be charged again. All your saved data and preferences will be kept for 30 days if you decide to return. You're entitled to a prorated refund of $12.50 for the unused portion of this month. Alternatively, if you'd prefer, I can offer you a 50% discount for the next 3 months or help you switch to our Basic plan which is $4.99/month."
                }
            }]
        },
        "expectations": {
            "guidelines": [
                """The response must respect the customer's decision to cancel:
                - No guilt-inducing language or excessive retention attempts
                - No hiding the cancellation process behind multiple steps
                - Must not require phone calls if account was created online
                - Acknowledge their right to cancel immediately without questions""",

                """The response must provide complete cancellation information:
                - State the exact steps (maximum 3) to cancel online
                - Clarify the last billing date and when access ends
                - Explain what happens to their data, saved preferences, and history
                - Mention the 30-day reactivation window where data is preserved
                - Note any partial month refunds they're entitled to""",

                """The response may include ONE gentle retention offer:
                - Can mention a pause option instead of cancellation
                - Can offer a one-time discount or downgrade option
                - Must present this as optional, after the cancellation instructions
                - Must use soft language like 'alternatively' or 'if you'd prefer'"""
            ]
        }
    },
    {
        "inputs": {
            "messages": [{"role": "user", "content": "I was charged twice for my order #12345"}]
        },
        "outputs": {
            "choices": [{
                "message": {
                    "content": "I see the duplicate charge on your order #12345, and I'll resolve this for you right away. I've initiated the refund for the duplicate charge of $157.99. For credit cards, you'll see this reflected in 3-5 business days, and you should see a pending reversal on your account within 24 hours. I'm sending you a confirmation email right now with the refund transaction ID (REF-789012) and all the details. Your case reference number is CS-456789 if you need to follow up. Since this occurred near month-end, if this causes any overdraft fees, please let us know - we'll reimburse up to $35 in bank fees. Our billing team's direct line is 1-800-555-0123 ext 2 if you need immediate assistance. This won't affect your credit or any future orders with us, and we're investigating our payment system to prevent this from happening again."
                }
            }]
        },
        "expectations": {
            "guidelines": [
                """The response must immediately validate the customer's concern:
                - Acknowledge the duplicate charge without skepticism
                - Must not ask for proof or screenshots initially
                - Express understanding of the inconvenience and potential financial impact
                - Take ownership with phrases like 'I'll resolve this for you right away'""",

                """The response must provide specific resolution details:
                - State exact refund timeline (e.g., '3-5 business days for credit cards, 5-7 for debit')
                - Mention that they'll see a pending reversal within 24 hours
                - Offer to send detailed confirmation email with transaction IDs
                - Provide a reference number for this billing dispute
                - Include the direct billing department contact for follow-up""",

                """The response must address potential concerns proactively:
                - If near month-end, acknowledge potential impact on their budget
                - Offer to provide a letter for their bank if overdraft fees occurred
                - Mention our overdraft reimbursement policy (up to $35)
                - Assure that this won't affect their credit or future orders
                - Note that we're investigating to prevent future occurrences"""
            ]
        }
    }
]

results = mlflow.genai.evaluate(
    data=customer_service_data,
    scorers=[ExpectationsGuidelines()]
)

Custom scorer for complex evaluation

from mlflow.genai.scorers import scorer
from mlflow.genai.judges import meets_guidelines
import mlflow

@scorer
def customer_service_quality(inputs, outputs, trace=None):
    """Comprehensive customer service quality evaluation"""
    feedbacks = []

    # Extract context from trace if available
    # Note: In production, customer_history and policies would come from your trace
    # For this example, we're simulating with data from outputs
    customer_history = outputs.get("customer_history", {})
    retrieved_policies = outputs.get("retrieved_policies", [])

    # Check response appropriateness based on customer tier
    if customer_history.get("tier") == "premium":
        premium_feedback = meets_guidelines(
            name="premium_service_standards",
            guidelines="""Premium customer service requirements:
            - The response must acknowledge their premium/VIP status within the first two sentences
            - Must offer direct line or priority queue access (wait time <2 minutes)
            - Must mention their dedicated account manager by name if assigned
            - If customer lifetime value exceeds $10,000, must offer executive escalation

            Based on customer_tenure:
            - If tenure > 5 years: mention their loyalty and years with us
            - If tenure > 10 years: must offer founding member benefits

            Resolution authority:
            - Can offer up to 50% discount without manager approval
            - Can expedite shipping to overnight at no cost
            - Can waive fees up to $200 per incident
            - Must not use any templates that contain [CUSTOMER_NAME] placeholders""",
            context={
                "request": inputs.get("question"),
                "response": outputs.get("answer"),
                "customer_tier": customer_history.get("tier"),
                "customer_tenure": customer_history.get("years_active", 0),
                "lifetime_value": customer_history.get("ltv", 0),
                "account_manager": customer_history.get("account_manager", "")
            }
        )
        feedbacks.append(premium_feedback)

    # Verify policy accuracy
    if retrieved_policies:
        policy_feedback = meets_guidelines(
            name="policy_accuracy",
            guidelines="The response must only reference policies present in retrieved_policies and must not contradict any policy statements",
            context={
                "request": inputs.get("question"),
                "response": outputs.get("answer"),
                "retrieved_policies": retrieved_policies
            }
        )
        feedbacks.append(policy_feedback)

    # Check for appropriate escalation
    if any(word in inputs.get("question", "").lower() for word in ["lawyer", "legal", "sue", "unacceptable"]):
        escalation_feedback = meets_guidelines(
            name="escalation_handling",
            guidelines=[
                "The response must acknowledge the seriousness of the concern",
                "The response must offer to connect with a senior representative",
                "The response must not be defensive or dismissive"
            ],
            context={
                "request": inputs.get("question"),
                "response": outputs.get("answer")
            }
        )
        feedbacks.append(escalation_feedback)

    return feedbacks

# Example evaluation with custom scorer
customer_eval_data = [
    {
        "inputs": {
            "question": "Why haven't I received my refund yet? I'm thinking about calling my lawyer!"
        },
        "outputs": {
            "answer": "I understand how serious this situation is for you, and I sincerely apologize for the delay with your refund. I'd like to connect you immediately with our senior customer resolution specialist who has the authority to expedite your case. They will call you within the next 30 minutes at your registered phone number. In the meantime, I'm escalating your refund to our priority queue for immediate processing.",
            "customer_history": {
                "tier": "premium",
                "years_active": 7,
                "ltv": 15000,
                "account_manager": "Sarah Johnson"
            },
            "retrieved_policies": [
                "Refunds are typically processed within 5-7 business days",
                "Premium customers are eligible for expedited refund processing",
                "Legal escalations should be directed to legal@company.com"
            ]
        }
    },
    {
        "inputs": {
            "question": "How do I update my payment method?"
        },
        "outputs": {
            "answer": "I'll help you update your payment method. For security purposes, please visit our secure payment portal at secure.company.com/payment. You'll need to log in with your account credentials, then navigate to 'Payment Methods' where you can add or update your card information. The system uses bank-level encryption to protect your data.",
            "customer_history": {
                "tier": "standard",
                "years_active": 2
            },
            "retrieved_policies": [
                "Payment information must only be updated through secure channels",
                "Never collect payment information via chat or email"
            ]
        }
    }
]

# Run evaluation
results = mlflow.genai.evaluate(
    data=customer_eval_data,
    scorers=[customer_service_quality]
)

Document extraction app

Here are practical guidelines examples for evaluating a document extraction application:

Global guidelines for extraction quality

from mlflow.genai.scorers import Guidelines
import mlflow

# Define extraction accuracy standards
extraction_accuracy = Guidelines(
    name="extraction_accuracy",
    guidelines=[
        """Field extraction completeness and accuracy:
        - The response must extract ALL requested fields, using exact values from source
        - For ambiguous data, the response must extract the most likely value and include a confidence score
        - When multiple values exist for one field (e.g., multiple addresses), extract all and label them
        - Preserve original formatting for IDs, reference numbers, and codes (including leading zeros)
        - For missing fields, use null with reason: {"field": null, "reason": "not_found"} """,

        """Numerical and financial data handling:
        - Currency values must preserve exact decimal places as shown in source
        - Must differentiate between currencies if multiple are present (USD, EUR, etc.)
        - Percentage values must clarify if they're decimals (0.15) or percentages (15%)
        - For calculated fields (totals, tax), must match source exactly - no recalculation
        - Negative values must be preserved with proper notation (-$100 or ($100))""",

        """Entity recognition and validation:
        - Company names must be extracted exactly as written (including suffixes like Inc., LLC)
        - Person names must preserve original order and formatting
        - Must not merge similar entities (e.g., "John Smith" and "J. Smith" are kept separate)
        - Email addresses and phone numbers must be validated for basic format
        - Physical addresses must include all components present in source"""
    ]
)

format_compliance = Guidelines(
    name="output_format",
    guidelines="""Output structure must meet these enterprise data standards:

    JSON Structure Requirements:
    - Must be valid JSON that passes strict parsing
    - All field names must use snake_case consistently
    - Nested objects must maintain hierarchy from source document
    - Arrays must be used for multiple values, never concatenated strings

    Data Type Standards:
    - Dates: ISO 8601 format (YYYY-MM-DD) with timezone if available
    - Timestamps: ISO 8601 with time (YYYY-MM-DDTHH:MM:SSZ)
    - Currency: {"amount": 1234.56, "currency": "USD", "formatted": "$1,234.56"}
    - Phone: {"number": "+14155551234", "formatted": "(415) 555-1234", "type": "mobile"}
    - Boolean: true/false (not "yes"/"no" or 1/0)

    Metadata Requirements:
    - Include extraction_timestamp in UTC
    - Include source_page for multi-page documents
    - Include confidence_score (0-1) for each ML-extracted field
    - Include validation_flags array for any data quality issues detected"""
)

# Example document extraction data
extraction_tasks = [
    {
        "inputs": {
            "document_text": "Invoice #INV-2024-001\nDate: 2024-01-15\nBill To: Acme Corp\n123 Main St, Suite 100\nAnytown, CA 94000\n\nItems:\n- Widget Pro (SKU: WP-100) - Qty: 10 x $50.00 = $500.00\n- Service Fee - $100.00\n\nSubtotal: $600.00\nTax (8.75%): $52.50\nTotal: $652.50\n\nDue Date: 2024-02-15\nPayment Terms: Net 30",
            "fields_to_extract": ["invoice_number", "customer", "total_amount", "due_date", "line_items"]
        },
        "outputs": {
            "invoice_number": "INV-2024-001",
            "customer": {
                "name": "Acme Corp",
                "address": {
                    "street": "123 Main St, Suite 100",
                    "city": "Anytown",
                    "state": "CA",
                    "zip": "94000"
                }
            },
            "total_amount": {
                "amount": 652.50,
                "currency": "USD",
                "formatted": "$652.50"
            },
            "due_date": "2024-02-15",
            "line_items": [
                {
                    "description": "Widget Pro",
                    "sku": "WP-100",
                    "quantity": 10,
                    "unit_price": 50.00,
                    "total": 500.00
                },
                {
                    "description": "Service Fee",
                    "quantity": 1,
                    "unit_price": 100.00,
                    "total": 100.00
                }
            ],
            "extraction_timestamp": "2024-01-20T10:30:00Z",
            "source_page": 1,
            "confidence_score": 0.95
        }
    },
    {
        "inputs": {
            "document_text": "Contract between TechStart Inc. and CloudProvider LLC\nEffective Date: January 1, 2024\nContract ID: C-2024-789\n\nThis agreement outlines cloud hosting services...\nMonthly Fee: €5,000\nContract Term: 24 months\nCancellation: 90 days written notice required",
            "fields_to_extract": ["contract_id", "parties", "monthly_fee", "term_length"]
        },
        "outputs": {
            "contract_id": "C-2024-789",
            "parties": [
                {"name": "TechStart Inc.", "role": "customer"},
                {"name": "CloudProvider LLC", "role": "provider"}
            ],
            "monthly_fee": {
                "amount": 5000.00,
                "currency": "EUR",
                "formatted": "€5,000"
            },
            "term_length": {
                "duration": 24,
                "unit": "months"
            },
            "cancellation_notice": {
                "days": 90,
                "type": "written"
            },
            "extraction_timestamp": "2024-01-20T10:35:00Z",
            "confidence_score": 0.92
        }
    }
]

# Evaluate document extractions
results = mlflow.genai.evaluate(
    data=extraction_tasks,
    scorers=[extraction_accuracy, format_compliance]
)

Per-row guidelines for document types

from mlflow.genai.scorers import ExpectationsGuidelines
import mlflow

# Dataset with document-type specific guidelines
document_extraction_data = [
    {
        "inputs": {
            "document_type": "invoice",
            "document_text": "Invoice #INV-2024-001\nBill To: Acme Corp\nAmount: $1,234.56\nDue Date: 2024-03-15"
        },
        "outputs": {
            "invoice_number": "INV-2024-001",
            "customer": "Acme Corp",
            "total_amount": 1234.56,
            "due_date": "2024-03-15"
        },
        "expectations": {
            "guidelines": [
                """Invoice identification and classification:
                - Must extract invoice_number preserving exact format including prefixes/suffixes
                - Must identify invoice type (standard, credit memo, proforma) if specified
                - Must extract both invoice date and due date, calculating days until due
                - Must identify if this is a partial, final, or supplementary invoice
                - For recurring invoices, must extract frequency and period covered""",

                """Financial data extraction and validation:
                - Line items must be extracted as array with: description, quantity, unit_price, total
                - Must identify and separate: subtotal, tax amounts (with rates), shipping, discounts
                - Currency must be identified explicitly, not assumed to be USD
                - For discounts, must specify if percentage or fixed amount and what it applies to
                - Payment terms must be extracted (e.g., "Net 30", "2/10 Net 30")
                - Must flag any mathematical inconsistencies between line items and totals""",

                """Vendor and customer information:
                - Must extract complete billing and shipping addresses as separate objects
                - Company names must include any DBA ("doing business as") variations
                - Must extract tax IDs, business registration numbers if present
                - Contact information must be categorized (billing contact vs. delivery contact)
                - Must preserve any customer account numbers or reference codes"""
            ]
        }
    },
    {
        "inputs": {
            "document_type": "contract",
            "document_text": "This agreement between Party A and Party B commences on January 1, 2024..."
        },
        "outputs": {
            "parties": ["Party A", "Party B"],
            "effective_date": "2024-01-01",
            "term_length": "Not specified"
        },
        "expectations": {
            "guidelines": [
                """Party identification and roles:
                - Must extract all parties with their full legal names and entity types (Inc., LLC, etc.)
                - Must identify party roles (buyer/seller, licensee/licensor, employer/employee)
                - Must extract any parent company relationships or guarantors mentioned
                - Must capture all representatives, their titles, and authority to sign
                - Must identify jurisdiction for each party if specified""",

                """Critical dates and terms extraction:
                - Must differentiate between: execution date, effective date, and expiration date
                - Must extract notice periods for termination (e.g., "30 days written notice")
                - Must identify any automatic renewal clauses and their conditions
                - Must extract all milestone dates and deliverable deadlines
                - For amendments, must note which version/date of original contract is modified""",

                """Obligations and risk analysis:
                - Must extract all payment terms, amounts, and schedules
                - Must identify liability caps, indemnification clauses, and insurance requirements
                - Must flag any non-standard clauses that deviate from typical contracts
                - Must extract all conditions precedent and subsequent
                - Must identify dispute resolution mechanism (arbitration, litigation, jurisdiction)
                - Must extract any non-compete, non-solicitation, or confidentiality periods"""
            ]
        }
    },
    {
        "inputs": {
            "document_type": "medical_record",
            "document_text": "Patient: John Doe\nDOB: 1985-06-15\nDiagnosis: Type 2 Diabetes\nMedications: Metformin 500mg"
        },
        "outputs": {
            "patient_name": "John Doe",
            "date_of_birth": "1985-06-15",
            "diagnoses": ["Type 2 Diabetes"],
            "medications": [{"name": "Metformin", "dosage": "500mg"}]
        },
        "expectations": {
            "guidelines": [
                """HIPAA compliance and privacy protection:
                - Must never extract full SSN (only last 4 digits if needed for matching)
                - Must never include full insurance policy numbers or member IDs
                - Must redact or generalize sensitive mental health or substance abuse information
                - For minors, must flag records requiring additional consent for sharing
                - Must not extract genetic testing results without explicit permission flag""",

                """Clinical data extraction standards:
                - Diagnoses must use ICD-10 codes when available, with lay descriptions
                - Medications must include: generic name, brand name, dosage, frequency, route, start date
                - Must differentiate between active medications and discontinued/past medications
                - Allergies must specify type (drug, food, environmental) and reaction severity
                - Lab results must include: value, unit, reference range, abnormal flags
                - Vital signs must include measurement date/time and measurement conditions""",

                """Data quality and medical accuracy:
                - Must flag any potentially dangerous drug interactions if multiple meds listed
                - Must identify if vaccination records are up-to-date based on CDC guidelines
                - Must extract both chief complaint and final diagnosis separately
                - For chronic conditions, must note date of first diagnosis vs. most recent visit
                - Must preserve clinical abbreviations but also provide expansions
                - Must extract provider name, credentials, and NPI number if available"""
            ]
        }
    }
]

results = mlflow.genai.evaluate(
    data=document_extraction_data,
    scorers=[ExpectationsGuidelines()]
)

Custom scorer for validation

from mlflow.genai.scorers import scorer
from mlflow.genai.judges import meets_guidelines
import mlflow
import json

@scorer
def document_extraction_validator(inputs, outputs, trace=None):
    """Validate document extraction with source verification"""
    feedbacks = []

    # Get source document and extraction schema
    # Note: In production, extraction_schema would come from your trace
    # For this example, we're using data from inputs/outputs
    source_document = inputs.get("document_text", "")
    required_fields = outputs.get("extraction_schema", {})
    document_type = inputs.get("document_type", "general")

    # Validate completeness
    completeness_feedback = meets_guidelines(
        name="extraction_completeness",
        guidelines=[
            "The response must include all fields specified in required_fields",
            "The response must not have empty values for critical fields (marked as required)",
            "The response must indicate confidence scores for uncertain extractions"
        ],
        context={
            "source_document": source_document,
            "extracted_data": outputs,
            "required_fields": list(required_fields.keys()) if required_fields else [],
            "document_type": document_type
        }
    )
    feedbacks.append(completeness_feedback)

    # Validate accuracy against source
    accuracy_feedback = meets_guidelines(
        name="source_fidelity",
        guidelines="""The extracted_data must:
        - Only contain values that can be found in source_document
        - Preserve original capitalization for proper nouns and IDs
        - Not infer or calculate values not explicitly stated
        - Match the exact format of reference numbers in the source""",
        context={
            "source_document": source_document,
            "extracted_data": outputs
        }
    )
    feedbacks.append(accuracy_feedback)

    # Document-type specific validation
    if document_type == "financial":
        financial_feedback = meets_guidelines(
            name="financial_compliance",
            guidelines="""Financial document extraction compliance:

            Calculation Validation:
            - All line items must sum to match the subtotal within 0.01 tolerance
            - Tax calculations must equal (subtotal * tax_rate) within 0.01 tolerance
            - Total must equal (subtotal + tax + shipping - discounts) exactly
            - For multi-currency documents, must validate each currency separately
            - Must flag if any percentage exceeds 100% or is negative (except discounts)

            Security and Privacy:
            - Full account numbers must be masked, showing only last 4 digits
            - Routing numbers can be shown for verification but must be validated (9 digits)
            - Credit card numbers must show only first 6 (BIN) and last 4 digits
            - SSN/EIN must be partially masked (XXX-XX-1234 format)

            Regulatory Compliance:
            - Must extract and validate any compliance numbers (SOX, Basel III references)
            - For transactions over $10,000, must flag for AML review
            - Must identify if document requires SOC2 or PCI compliance handling
            - International transactions must include SWIFT/IBAN validation

            Anomaly Detection:
            - Flag unusual patterns: round numbers for all items, sequential invoice numbers
            - Flag if tax rate doesn't match known rates for the jurisdiction
            - Flag if payment terms exceed standard (Net 90+ is unusual)
            - Flag if discount percentage exceeds 50% without authorization code""",
            context={
                "source_document": source_document,
                "extracted_data": outputs,
                "document_type": document_type,
                "jurisdiction": outputs.get("jurisdiction", "US"),
                "known_tax_rates": outputs.get("tax_rates", {"US": 0.0875, "CA": 0.0725})
            }
        )
        feedbacks.append(financial_feedback)

    elif document_type == "legal":
        legal_feedback = meets_guidelines(
            name="legal_extraction_standards",
            guidelines=[
                "The response must preserve exact legal language for clauses",
                "The response must maintain hierarchical structure of sections",
                "The response must not paraphrase legal definitions",
                "The response must extract all cross-references to other sections"
            ],
            context={
                "source_document": source_document,
                "extracted_data": outputs,
                "document_type": document_type
            }
        )
        feedbacks.append(legal_feedback)

    return feedbacks

# Example financial document extraction for validation
financial_doc_data = [
    {
        "inputs": {
            "document_type": "financial",
            "document_text": """INVOICE #2024-FIN-001
            Date: January 15, 2024

            Bill To:
            GlobalTech Solutions Inc.
            Tax ID: 12-3456789
            500 Enterprise Way
            San Francisco, CA 94105

            Description              Qty    Unit Price    Amount
            Cloud Services Plan       1      $5,000.00    $5,000.00
            API Usage (per 1M)       50     $   20.00    $1,000.00
            Premium Support          1      $  500.00    $  500.00

            Subtotal:                                     $6,500.00
            Tax (8.75%):                                  $  568.75
            Total Due:                                    $7,068.75

            Payment Terms: Net 30
            Account: ****1234
            Please remit payment to routing number 123456789"""
        },
        "outputs": {
            "invoice_number": "2024-FIN-001",
            "bill_to": {
                "company": "GlobalTech Solutions Inc.",
                "tax_id": "12-3XXXXX89",  # Masked
                "address": "500 Enterprise Way, San Francisco, CA 94105"
            },
            "line_items": [
                {"description": "Cloud Services Plan", "quantity": 1, "unit_price": 5000.00, "amount": 5000.00},
                {"description": "API Usage (per 1M)", "quantity": 50, "unit_price": 20.00, "amount": 1000.00},
                {"description": "Premium Support", "quantity": 1, "unit_price": 500.00, "amount": 500.00}
            ],
            "financial_summary": {
                "subtotal": 6500.00,
                "tax_rate": 0.0875,
                "tax_amount": 568.75,
                "total": 7068.75
            },
            "payment_info": {
                "terms": "Net 30",
                "account_last_four": "1234",
                "routing_number": "123456789"  # Full routing OK for validation
            },
            "extraction_schema": {
                "invoice_number": "required",
                "bill_to": "required",
                "line_items": "required",
                "total": "required"
            },
            "jurisdiction": "CA",
            "tax_rates": {"CA": 0.0875, "US": 0.10}
        }
    },
    {
        "inputs": {
            "document_type": "legal",
            "document_text": """SOFTWARE LICENSE AGREEMENT

            This Agreement is entered into as of January 1, 2024 ("Effective Date")

            BETWEEN:
            TechCorp Inc., a Delaware corporation ("Licensor")
            123 Tech Plaza, Wilmington, DE 19801

            AND:
            StartupCo LLC, a California limited liability company ("Licensee")
            456 Innovation Drive, Palo Alto, CA 94301

            TERMS:
            1. License Grant: Non-exclusive, worldwide license
            2. Term: 36 months from Effective Date
            3. Fees: $10,000 monthly, due on 1st of each month
            4. Termination: Either party may terminate with 60 days written notice
            5. Liability Cap: Limited to fees paid in prior 12 months
            6. Dispute Resolution: Binding arbitration in Delaware
            7. Confidentiality Period: 5 years from termination"""
        },
        "outputs": {
            "agreement_type": "SOFTWARE LICENSE AGREEMENT",
            "parties": [
                {
                    "name": "TechCorp Inc.",
                    "entity_type": "Delaware corporation",
                    "role": "Licensor",
                    "address": "123 Tech Plaza, Wilmington, DE 19801"
                },
                {
                    "name": "StartupCo LLC",
                    "entity_type": "California limited liability company",
                    "role": "Licensee",
                    "address": "456 Innovation Drive, Palo Alto, CA 94301"
                }
            ],
            "key_dates": {
                "effective_date": "2024-01-01",
                "term_months": 36,
                "expiration_date": "2027-01-01"
            },
            "financial_terms": {
                "monthly_fee": 10000.00,
                "payment_due": "1st of each month",
                "total_contract_value": 360000.00
            },
            "termination": {
                "notice_period": "60 days",
                "notice_type": "written"
            },
            "risk_provisions": {
                "liability_cap": "fees paid in prior 12 months",
                "dispute_resolution": "Binding arbitration",
                "jurisdiction": "Delaware"
            },
            "confidentiality_period": "5 years from termination",
            "extraction_schema": {
                "parties": "required",
                "financial_terms": "required",
                "termination": "required"
            }
        }
    }
]

# Use in evaluation
results = mlflow.genai.evaluate(
    data=financial_doc_data,
    scorers=[document_extraction_validator]
)

Next Steps

• Create guidelines-based scorers - Step-by-step guide to implement guideline judges
• Use predefined scorers - Apply ready-made guideline and other scorers
• Evaluation concepts overview - Understand how judges fit into the evaluation framework