Source: data_layer/docs/README_CONTRACT_OPTIMIZATION.md

Contract Generation Optimization System

Overview

This system optimizes contract generation through:

Hierarchical Contract Model - Reduces repetition via inheritance
Contract Fingerprinting - Learns from real, successful contracts
Feedback Loop System - Captures user satisfaction and continuously improves
Layered Context Injection - Progressive enrichment from multiple sources

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    Contract Generation Flow                      │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 1: Base Structure (Hierarchical Model)                   │
│  • Template inheritance (premium/standard/basic/enterprise)     │
│  • Common patterns shared across tiers                          │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 2: Tier Preset (Commercial Terms)                        │
│  • Base financial terms for tier                                │
│  • Subtier progression and feature gates                        │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 3: Sport Modifier (Archetype Adjustments)                │
│  • Complexity and data richness multipliers                     │
│  • Sport-specific term adjustments                              │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 4: Fingerprint Pattern (Real Contract Learning)          │
│  • Language patterns from similar contracts                     │
│  • Commercial structures that closed successfully               │
│  • Key learnings from past deals                                │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 5: Negotiation History (Deal Intelligence)               │
│  • Common pushback points and responses                         │
│  • Anti-patterns to avoid                                       │
│  • Successful negotiation strategies                            │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 6: Feedback Learning (Continuous Improvement)            │
│  • User satisfaction data from similar contracts                │
│  • Improvement opportunities identified                         │
│  • Pain points to address                                       │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│  Layer 7: Real-time Context (Session State)                     │
│  • User preferences and previous iterations                     │
│  • Adjustments requested in this session                        │
│  • Current negotiation state                                    │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│                   Generated Contract                             │
│  • Context-enriched from all 7 layers                           │
│  • Optimized for user's specific situation                      │
│  • Incorporates learning from past successes                    │
└─────────────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│              Feedback Collection (Critical Points)               │
│  • Contract generated ────────► Satisfaction rating             │
│  • Contract reviewed  ────────► Qualitative feedback            │
│  • Negotiation points ────────► Improvement suggestions         │
│  • Contract signed    ────────► Success metrics                 │
│  • Ongoing relationship ───────► Long-term satisfaction         │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
┌─────────────────────────────────────────────────────────────────┐
│           Feedback Analysis & Pattern Extraction                 │
│  • Aggregate trends across contracts                            │
│  • Identify success predictors                                  │
│  • Generate improvement recommendations                         │
│  • Update fingerprint library                                   │
└──────────────────────────┬──────────────────────────────────────┘
                           ▼
                 [Feeds back to Layer 6]

Key Components

1. Hierarchical Contract Model (`contract_model_v4_hierarchical.json`)

Purpose: Eliminate repetition by using inheritance and composition

Key Features:

Base templates that define common structure
Tier archetypes (premium, standard, basic, enterprise)
Sport modifiers for archetype-specific adjustments
Subtier progression rules for financial scaling

Benefits:

80% reduction in JSON size
Single source of truth for common patterns
Easy to update global changes
Maintains tier-specific customizations

Usage:

from contextual_contract_builder import ContextualContractBuilder
 
builder = ContextualContractBuilder()
context = builder.build_contract_context(
    league_name="Example League",
    sport_type="Soccer",
    tier="1.3",
    sports_archetype="field_sport",
    questionnaire_data={...}
)

2. Contract Fingerprinting (`contract_fingerprints.json`)

Purpose: Extract and reuse patterns from real, successful contracts

Fingerprint Schema:

Structural patterns: Section order, clause density, complexity
Language patterns: Tone, recurring phrases, legal terminology
Commercial patterns: Fee structures, payment triggers, escalations
Relationship patterns: How partnership is framed
Success metrics: Time to signature, satisfaction scores, outcomes

Real Contract Sources:

ASD_NLL_LongForm_071025.docx → Premium partnership (Tier 1.3)
ASD_GRASSLEAGUE_V2TermSheet_062625.docx → Growth partnership (Tier 2.1)
ASD_TermSheet_WORLDSEVENS_073025.docx → Enterprise global (Tier 1.4)

Adding New Fingerprints:

{
  "contract_id": "LEAGUE_DATE",
  "source_document": "path/to/contract.docx",
  "league_name": "League Name",
  "tier_assigned": "1.3",
  "sports_archetype": "stick_sport",
  "structural_patterns": {...},
  "language_patterns": {...},
  "commercial_patterns": {...},
  "success_metrics": {
    "was_signed": true,
    "time_to_signature_days": 42,
    "satisfaction_score": 4.7
  },
  "key_learnings": [
    "What worked well in this deal",
    "What could be improved",
    "Unique insights from this relationship"
  ]
}

3. Feedback Loop System (`feedback_loop_system.py`)

Purpose: Capture user satisfaction at critical interaction points

Critical Interaction Points:

QUESTIONNAIRE_COMPLETE - After league fills out intake form
LEAGUE_CLASSIFIED - After tier/archetype assignment
TIER_ASSIGNED - After pricing calculation
CONTRACT_GENERATED - After initial contract creation
CONTRACT_REVIEWED - After first review by league
NEGOTIATION_COMPLETE - After negotiation finalization
CONTRACT_SIGNED - After contract execution
ONBOARDING_COMPLETE - After technical onboarding
SIX_MONTH_REVIEW - Mid-contract check-in
RENEWAL_DECISION - Contract renewal/termination

Feedback Types:

SATISFACTION: 1-5 rating
QUALITATIVE: Open text feedback
IMPROVEMENT: Specific suggestions
BUG_REPORT: Issues encountered
PRAISE: What worked well
COMPLAINT: What didn't work

Integration Example:

from feedback_loop_system import FeedbackCollector, InteractionPoint
 
collector = FeedbackCollector()
 
# After contract generation
collector.capture_feedback(
    interaction_point=InteractionPoint.CONTRACT_GENERATED,
    contract_id="NLL_2025_001",
    league_name="National Lacrosse League",
    tier="1.3",
    sports_archetype="stick_sport",
    satisfaction_score=4.5,
    feedback_text="Contract looks great!",
    improvement_suggestions=["Add more fee schedule detail"],
    user_id="league_admin",
    session_id="session_123"
)
 
# Later: Generate summary
summary = collector.generate_contract_summary("NLL_2025_001")
print(f"Avg satisfaction: {summary.avg_satisfaction_score}/5")
print(f"Key learnings: {summary.key_learnings}")

4. Contextual Contract Builder (`contextual_contract_builder.py`)

Purpose: Progressive enrichment from multiple context sources

Layer Precedence:

Layer 7 (Real-time)
    ↓  overrides
Layer 6 (Feedback)
    ↓  overrides
Layer 5 (Negotiation History)
    ↓  overrides
Layer 4 (Fingerprints)
    ↓  overrides
Layer 3 (Sport Modifier)
    ↓  overrides
Layer 2 (Tier Preset)
    ↓  overrides
Layer 1 (Base Structure)

Building Complete Context:

from contextual_contract_builder import ContextualContractBuilder
 
builder = ContextualContractBuilder()
 
# Build context with all 7 layers
context = builder.build_contract_context(
    league_name="National Lacrosse League",
    sport_type="Lacrosse",
    tier="1.3",
    sports_archetype="stick_sport",
    questionnaire_data={
        "api_available": True,
        "real_time_feed": True,
        "events_per_year": 150
    },
    session_id="session_123",
    user_preferences={
        "tone": "formal",
        "detail_level": "comprehensive"
    }
)
 
# Generate enriched prompt for LLM
prompt = builder.generate_contract_prompt(context)
 
# Explain what each layer contributed
explanations = builder.explain_context_layers(context)
for layer_name, explanation in explanations.items():
    print(f"{layer_name}: {explanation['contribution']}")

Integration with Existing System

Step 1: Update Contract Generation Endpoint

# In your backend API
from contextual_contract_builder import ContextualContractBuilder
from feedback_loop_system import FeedbackCollector, InteractionPoint
 
builder = ContextualContractBuilder()
feedback = FeedbackCollector()
 
@app.post("/generate-contract")
async def generate_contract(request: ContractRequest):
    # Build context
    context = builder.build_contract_context(
        league_name=request.league_name,
        sport_type=request.sport_type,
        tier=request.tier,
        sports_archetype=request.sports_archetype,
        questionnaire_data=request.questionnaire_data,
        session_id=request.session_id
    )
    
    # Generate contract using LLM with enriched prompt
    prompt = builder.generate_contract_prompt(context)
    contract = await generate_with_llm(prompt)
    
    # Capture initial feedback opportunity
    # (User will provide rating after reviewing)
    
    return {
        "contract": contract,
        "context_explanations": builder.explain_context_layers(context),
        "feedback_prompt": "How satisfied are you with this contract? (1-5)"
    }
 
@app.post("/contract-feedback")
async def capture_contract_feedback(request: FeedbackRequest):
    # Capture feedback
    event = feedback.capture_feedback(
        interaction_point=InteractionPoint[request.interaction_point],
        contract_id=request.contract_id,
        league_name=request.league_name,
        tier=request.tier,
        sports_archetype=request.sports_archetype,
        satisfaction_score=request.satisfaction_score,
        feedback_text=request.feedback_text,
        improvement_suggestions=request.improvements,
        user_id=request.user_id,
        session_id=request.session_id
    )
    
    return {"status": "feedback_recorded", "event_id": event.event_id}

Step 2: Add Fingerprinting After Each Signed Contract

@app.post("/contract-signed")
async def contract_signed(request: SignedContractNotification):
    # Generate feedback summary
    summary = feedback.generate_contract_summary(request.contract_id)
    
    # Create fingerprint from successful contract
    fingerprint = {
        "contract_id": request.contract_id,
        "source_document": request.contract_path,
        "league_name": request.league_name,
        "tier_assigned": request.tier,
        "sports_archetype": request.sports_archetype,
        "success_metrics": {
            "was_signed": True,
            "time_to_signature_days": summary.time_to_signature_days,
            "amendments_count": summary.amendments_count,
            "satisfaction_score": summary.avg_satisfaction_score
        },
        "key_learnings": summary.key_learnings
        # ... extract more patterns from contract document
    }
    
    # Add to fingerprint library
    # (Manual review recommended before adding to production)
    save_fingerprint_draft(fingerprint)
    
    return {"status": "fingerprint_created"}

Step 3: Periodic Analysis & Improvement

from feedback_loop_system import ImprovementEngine
 
# Run weekly/monthly
@app.get("/improvement-analysis")
async def analyze_improvements():
    engine = ImprovementEngine(feedback)
    
    # Analyze all feedback
    trends = engine.analyze_trends()
    
    # Generate improvement recommendations
    recommendations = {
        "tier_adjustments": trends["tier_analysis"],
        "sport_adjustments": trends["sport_analysis"],
        "workflow_improvements": trends["stage_analysis"],
        "global_insights": trends["global_patterns"]
    }
    
    # Store for review
    save_improvement_report(recommendations)
    
    return recommendations

Workflow: From Questionnaire to Signed Contract

Phase 1: Intake & Classification

User fills questionnaire
    → Capture feedback (QUESTIONNAIRE_COMPLETE)
    → Classify tier & archetype
    → Capture feedback (LEAGUE_CLASSIFIED, TIER_ASSIGNED)

Phase 2: Contract Generation

Build context with 7 layers
    → Generate enriched contract
    → Capture feedback (CONTRACT_GENERATED)
    → User reviews contract
    → Capture feedback (CONTRACT_REVIEWED)
    → User requests adjustments?
        → Update context with adjustments
        → Regenerate contract
        → Capture feedback (iteration)

Phase 3: Negotiation & Signature

Negotiate terms
    → Capture feedback at key negotiation points
    → Finalize contract
    → Capture feedback (NEGOTIATION_COMPLETE)
    → Sign contract
    → Capture feedback (CONTRACT_SIGNED)
    → Generate contract summary
    → Create fingerprint (if successful)

Phase 4: Ongoing Relationship

Onboarding
    → Capture feedback (ONBOARDING_COMPLETE)
    → 6-month check-in
    → Capture feedback (SIX_MONTH_REVIEW)
    → Renewal decision
    → Capture feedback (RENEWAL_DECISION)
    → Update fingerprint with final metrics

Continuous Improvement Cycle

┌─────────────────────────────────────────────────────┐
│  1. Generate contracts with current best practices  │
└──────────────────┬──────────────────────────────────┘
                   ▼
┌─────────────────────────────────────────────────────┐
│  2. Capture user feedback at all critical points    │
└──────────────────┬──────────────────────────────────┘
                   ▼
┌─────────────────────────────────────────────────────┐
│  3. Analyze feedback trends and patterns            │
└──────────────────┬──────────────────────────────────┘
                   ▼
┌─────────────────────────────────────────────────────┐
│  4. Extract fingerprints from successful contracts  │
└──────────────────┬──────────────────────────────────┘
                   ▼
┌─────────────────────────────────────────────────────┐
│  5. Update context layers with new insights         │
└──────────────────┬──────────────────────────────────┘
                   ▼
┌─────────────────────────────────────────────────────┐
│  6. Next contract generation uses improved context  │
└──────────────────┬──────────────────────────────────┘
                   │
                   └──► (Repeat cycle)

Benefits Summary

1. Reduced Repetition

Before: 685 lines of highly repetitive tier definitions
After: ~200 lines with inheritance + composition
Maintenance: Update once, affects all relevant tiers

2. Learning from Real Contracts

Before: Generic templates without real-world validation
After: Patterns extracted from actual signed contracts
Result: Higher close rates, faster negotiations

3. Continuous Improvement

Before: Static templates, no learning mechanism
After: Feedback-driven improvements at every interaction
Result: System gets better with every contract

4. Layered Context

Before: One-size-fits-all approach
After: 7 layers of progressively richer context
Result: Highly personalized, situation-aware contracts

Next Steps

Extract More Fingerprints: Process all contracts in contract_terms_docx/ to build comprehensive fingerprint library
Integrate Feedback UI: Add satisfaction ratings and feedback forms at each critical interaction point in Streamlit app
LangMem Integration: Connect feedback system to persistent memory for long-term learning
A/B Testing: Test different contract variations and measure which perform better
Automated Fingerprinting: Build NLP pipeline to automatically extract patterns from signed contracts
Predictive Analytics: Use feedback data to predict contract success likelihood before sending

Files Reference

contract_model_v4_hierarchical.json - Hierarchical contract model
contract_fingerprints.json - Real contract patterns
feedback_loop_system.py - Feedback capture and analysis
contextual_contract_builder.py - Layered context builder
contract_term_presets.json - (Legacy, can be deprecated)
contract_prompt_template.txt - (Still used as output template)

Questions?

Contact the development team or refer to inline code documentation.

Consolidation Cleanup Guide Data Cleanup & Optimization Plan

Contract Generation Optimization System

Overview

Architecture

Key Components

1. Hierarchical Contract Model (`contract_model_v4_hierarchical.json`)

2. Contract Fingerprinting (`contract_fingerprints.json`)

3. Feedback Loop System (`feedback_loop_system.py`)

4. Contextual Contract Builder (`contextual_contract_builder.py`)

Integration with Existing System

Step 1: Update Contract Generation Endpoint

Step 2: Add Fingerprinting After Each Signed Contract

Step 3: Periodic Analysis & Improvement

Workflow: From Questionnaire to Signed Contract

Phase 1: Intake & Classification

Phase 2: Contract Generation

Phase 3: Negotiation & Signature

Phase 4: Ongoing Relationship

Continuous Improvement Cycle

Benefits Summary

1. Reduced Repetition

2. Learning from Real Contracts

3. Continuous Improvement

4. Layered Context

Next Steps

Files Reference

Questions?

Platform

Documentation

Community

Support

Contract Generation Optimization System

Overview

Architecture

Key Components

1. Hierarchical Contract Model (contract_model_v4_hierarchical.json)

2. Contract Fingerprinting (contract_fingerprints.json)

3. Feedback Loop System (feedback_loop_system.py)

4. Contextual Contract Builder (contextual_contract_builder.py)

Integration with Existing System

Step 1: Update Contract Generation Endpoint

Step 2: Add Fingerprinting After Each Signed Contract

Step 3: Periodic Analysis & Improvement

Workflow: From Questionnaire to Signed Contract

Phase 1: Intake & Classification

Phase 2: Contract Generation

Phase 3: Negotiation & Signature

Phase 4: Ongoing Relationship

Continuous Improvement Cycle

Benefits Summary

1. Reduced Repetition

2. Learning from Real Contracts

3. Continuous Improvement

4. Layered Context

Next Steps

Files Reference

Questions?

Platform

Documentation

Community

Support

1. Hierarchical Contract Model (`contract_model_v4_hierarchical.json`)

2. Contract Fingerprinting (`contract_fingerprints.json`)

3. Feedback Loop System (`feedback_loop_system.py`)

4. Contextual Contract Builder (`contextual_contract_builder.py`)