AI Requirements & Specifications - Learn Step by Step

🤔 Why AI Requirements Are Different

The $100 Million Requirements Mistake

Poor requirements are the #1 cause of AI project failure. 60% of AI projects fail due to unclear or unmeasurable requirements. The cost? Millions in wasted resources and lost opportunities.

🏥

Healthcare AI Disaster

A hospital system spent $50M on an AI diagnostic system. The requirement: "AI should diagnose diseases accurately."

What went wrong:

❌ No definition of "accurately" (90%? 99%?)
❌ No specification of which diseases
❌ No data requirements defined
❌ No edge cases considered

Result: System achieved 95% accuracy on common conditions but 30% on rare diseases. Unusable in production.

🛍️

Amazon's Success Story

Amazon's recommendation engine succeeds because of crystal-clear requirements:

✅ Specific metric: Increase purchase rate by 35%
✅ Clear constraints: Recommendations in < 100ms
✅ Measurable quality: 60% relevance score minimum
✅ Edge cases defined: New users, returning users, seasonal items

Result: 35% of revenue from recommendations, exactly as specified.

Traditional vs AI Requirements

Aspect	Traditional Software	AI Systems
Precision	Exact behavior: "Button saves data"	Probabilistic: "95% accuracy minimum"
Testing	Pass/Fail binary outcomes	Statistical performance ranges
Data Needs	Input/Output specifications	Training data quantity & quality
Edge Cases	Defined error handling	Graceful degradation strategies
Success Criteria	Features work as designed	Performance metrics achieved
Maintenance	Bug fixes and features	Continuous retraining needs

The Cost of Getting Requirements Wrong

Discovery Phase

1x

Cost to fix requirements here

Development

10x

Cost to fix during build

Testing

50x

Cost to fix in testing

Production

100x

Cost to fix after launch

🌟 Level 1: The Basics (Start Here!)

Level 1

The 4 Types of AI Requirements 📦

Think of AI requirements like building a smart robot. You need to specify different aspects:

📊 Data Requirements What it learns from

                                🤖
                                Model Requirements
                                How it thinks
                            

💼 Business Requirements What value it creates

⚙️ Technical Requirements How it operates

👥 User Requirements Who uses it & how

⚖️ Ethical Requirements Fairness & safety

Writing SMART Requirements for AI

SMART Requirements Template 🐍

# Bad Requirement ❌
requirement_bad = {
    "description": "The AI should be accurate",
    "measurable": False,
    "achievable": "Unknown",
    "testable": False
}

# Good Requirement ✅
requirement_good = {
    "id": "REQ-ML-001",
    "description": "Customer churn prediction model",
    "specific": "Predict customer churn 30 days in advance",
    "measurable": {
        "accuracy": 85,  # minimum %
        "precision": 80,  # minimum %
        "recall": 75,     # minimum %
        "latency_ms": 200  # maximum
    },
    "achievable": {
        "data_available": "2 years history, 100K customers",
        "baseline_performance": 70,  # current %
        "team_capability": True
    },
    "relevant": {
        "business_impact": "Reduce churn by 20%",
        "value": "$5M annual savings"
    },
    "time_bound": {
        "poc_date": "2024-03-01",
        "production_date": "2024-06-01"
    },
    "constraints": [
        "Must use existing customer data only",
        "Cannot use sensitive attributes (race, gender)",
        "Must provide explanation for predictions"
    ]
}

# Validation function
def validate_requirement(req):
    checks = []
    checks.append(len(req["specific"]) > 10)  # Has detail
    checks.append("measurable" in req)  # Has metrics
    checks.append("time_bound" in req)  # Has deadline
    
    return all(checks)
                                

⚠️ Common Beginner Mistake: Vague Success Metrics

Bad: "The model should perform well"

Problems:

What does "well" mean?
How do we test it?
When is it good enough?

✅ The Right Way: Specific & Measurable

Good: "The model must achieve:"

📊 Accuracy ≥ 90% on test set
⚡ Response time < 100ms for 95% of requests
💰 False positive rate < 5%
📈 Improve current baseline by 25%
🔄 Handle 1000 requests/second

🎮 Try It Yourself: Requirement Validator

Enter a requirement and check if it's well-formed:

🎨 Level 2: Common Requirement Patterns

Intermediate

Pattern 1: MoSCoW Prioritization for AI

Not all requirements are equal. Use MoSCoW to prioritize:

MUST Have

60%

Core functionality
Minimum accuracy
Legal compliance
Data privacy

SHOULD Have

20%

Better performance
Additional features
UI improvements
Extended coverage

COULD Have

10%

Nice-to-have features
Advanced analytics
Extra integrations
Cosmetic changes

WON'T Have

10%

Future releases
Out of scope
Too expensive
Not feasible now

Pattern 2: User Story Template for AI Features

AI User Story Framework

# Traditional User Story
traditional_story = {
    "as_a": "Customer Service Manager",
    "i_want": "To see customer sentiment",
    "so_that": "I can prioritize responses"
}

# AI-Enhanced User Story (Better!)
ai_story = {
    "as_a": "Customer Service Manager",
    "i_want": "AI to analyze customer sentiment in real-time",
    "so_that": "I can route angry customers to senior agents",
    
    # AI-Specific Additions
    "accuracy_needed": "85% sentiment classification accuracy",
    "data_requirements": {
        "training_data": "10,000 labeled customer messages",
        "languages": ["English", "Spanish"],
        "update_frequency": "Weekly retraining"
    },
    "acceptance_criteria": [
        "Sentiment scores available within 2 seconds",
        "Handle 500 concurrent conversations",
        "Provide confidence score with each prediction",
        "Flag messages needing human review"
    ],
    "edge_cases": [
        "Sarcasm detection",
        "Mixed sentiment messages",
        "Emoji interpretation",
        "Slang and abbreviations"
    ],
    "fallback_behavior": "Route to human when confidence < 70%"
}

# Generate acceptance tests from story
def generate_tests(story):
    tests = []
    for criterion in story["acceptance_criteria"]:
        tests.append({
            "test_name": f"Test: {criterion}",
            "expected": criterion,
            "status": "pending"
        })
    return tests
                                

Pattern 3: The Requirements Traceability Matrix

Requirement ID	Description	Priority	Data Needed	Model Type	Success Metric	Test Case
REQ-001	Detect fraud	MUST	Transaction history	Classification	95% recall	✓
REQ-002	Real-time scoring	MUST	Live stream	Inference API	<100ms latency	✓
REQ-003	Explain decisions	SHOULD	Feature importance	XAI module	User satisfaction	✓
REQ-004	Multi-language	COULD	Multilingual data	NLP model	80% accuracy	-

Pattern 4: Data Requirements Specification

DATA-REQ-001 MUST HAVE

Training Data Requirements

Volume: Minimum 100,000 labeled examples
Quality: Less than 5% label noise
Distribution: 60% negative, 40% positive class
Recency: Data from last 2 years
Features: 50+ attributes per record
Format: CSV, JSON, or Parquet
Updates: Daily incremental updates

💪 Practice: Build Your Requirements Document

🚀 Level 3: Advanced Requirements Engineering

Advanced

Advanced Strategy 1: Edge Case Documentation

Comprehensive Edge Case Framework

# Advanced: Edge case requirement specification
class EdgeCaseRequirements:
    
    def __init__(self, model_type):
        self.model_type = model_type
        self.edge_cases = []
        
    def define_edge_cases(self):
        # Example for fraud detection system
        edge_cases = {
            "data_edge_cases": [
                {
                    "case": "New user with no history",
                    "frequency": "10% of users",
                    "requirement": "Use demographic model, confidence > 60%",
                    "fallback": "Manual review for transactions > $500"
                },
                {
                    "case": "Sudden behavior change",
                    "frequency": "2% of transactions",
                    "requirement": "Flag for review if change > 3 std dev",
                    "fallback": "Send verification to user"
                }
            ],
            "performance_edge_cases": [
                {
                    "case": "Black Friday traffic spike",
                    "load": "10x normal",
                    "requirement": "Maintain <500ms response",
                    "strategy": "Auto-scale to 50 instances"
                },
                {
                    "case": "Model server failure",
                    "probability": "0.1% daily",
                    "requirement": "Failover in <10 seconds",
                    "strategy": "Hot standby + rule-based backup"
                }
            ],
            "ethical_edge_cases": [
                {
                    "case": "Protected class discrimination",
                    "requirement": "Equal approval rates ±5% across groups",
                    "monitoring": "Daily fairness metrics",
                    "correction": "Automatic rebalancing"
                }
            ]
        }
        return edge_cases
    
    def generate_test_scenarios(self, edge_case):
        # Create specific test cases for each edge case
        test_scenarios = []
        for case in edge_case["data_edge_cases"]:
            test_scenarios.append({
                "scenario": case["case"],
                "test_data": self.generate_synthetic_data(case),
                "expected_behavior": case["requirement"],
                "validation_method": "automated"
            })
        return test_scenarios

# Key insight: Document edge cases as requirements, not afterthoughts
                                

Advanced Strategy 2: Explainability Requirements

XAI-REQ-001 MUST HAVE

Explainability Requirements for Credit Scoring

Global Explainability:
- Feature importance ranking for entire model
- Decision boundary visualization
- Performance across different segments
Local Explainability:
- Top 3 factors for each decision
- Counterfactual examples ("What if" scenarios)
- Confidence intervals for predictions
Regulatory Compliance:
- GDPR Article 22: Right to explanation
- FCRA: Adverse action reasons
- Audit trail for all decisions

Advanced Strategy 3: Performance Degradation Requirements

Degradation Type	Detection Method	Threshold	Response
Data Drift	KL Divergence monitoring	KL > 0.1	Alert + Retraining evaluation
Concept Drift	Accuracy on holdout set	Drop > 5%	Automatic retraining trigger
Performance Decay	Rolling window metrics	Below baseline for 7 days	Rollback to previous version
Adversarial Drift	Anomaly detection	Spike in unusual patterns	Enhanced validation mode

⚠️ Advanced Pitfall: Over-Specifying Requirements

Problem: Requirements so detailed they constrain innovation

Example: "Must use Random Forest with exactly 100 trees"

Better: "Must achieve 90% accuracy with interpretable model"

Key: Specify the "what" and "why", not the "how"

📖 Quick Reference

Requirements Template Library

TEMPLATE-001 Classification Model Requirements

REQUIREMENT ID: [REQ-ML-XXX]
TYPE: Classification Model
PRIORITY: [MUST/SHOULD/COULD/WON'T]

FUNCTIONAL REQUIREMENTS:
- Model shall classify [target] into [N] categories
- Accuracy: ≥ [X]%
- Precision: ≥ [X]%
- Recall: ≥ [X]%
- F1 Score: ≥ [X]

DATA REQUIREMENTS:
- Training samples: ≥ [N]
- Class distribution: [balanced/imbalanced specification]
- Features: [list required features]
- Update frequency: [batch/streaming/real-time]

PERFORMANCE REQUIREMENTS:
- Inference time: < [X]ms for 95th percentile
- Throughput: > [X] predictions/second
- Model size: < [X]MB
- Memory usage: < [X]GB

CONSTRAINTS:
- Must not use [protected attributes]
- Must provide confidence scores
- Must be auditable
- Must support incremental learning

ACCEPTANCE CRITERIA:
- [ ] Passes all unit tests
- [ ] Meets performance benchmarks
- [ ] Validated on holdout test set
- [ ] Bias assessment completed
- [ ] Documentation complete

Common Metrics Reference

Metric	Use Case	Good Range	Formula/Description
Accuracy	Balanced datasets	> 85%	(TP + TN) / Total
Precision	When false positives are costly	> 90%	TP / (TP + FP)
Recall	When false negatives are costly	> 85%	TP / (TP + FN)
F1 Score	Balance precision and recall	> 80%	2 * (Precision * Recall) / (Precision + Recall)
AUC-ROC	Ranking quality	> 0.8	Area under ROC curve
MAE	Regression problems	Domain-specific	Mean Absolute Error
RMSE	Penalize large errors	Domain-specific	Root Mean Square Error
Latency	Real-time systems	< 100ms	P95 response time

Requirements Checklist Generator

Quick Requirements Validator

# Use this checklist for every requirement

COMPLETENESS CHECKLIST:
□ Specific: No ambiguous terms
□ Measurable: Has numeric targets
□ Achievable: Validated feasibility
□ Relevant: Links to business goal
□ Time-bound: Has deadline

DATA CHECKLIST:
□ Volume specified
□ Quality standards defined
□ Sources identified
□ Privacy compliance verified
□ Update frequency defined

TECHNICAL CHECKLIST:
□ Performance targets set
□ Scalability requirements defined
□ Integration points specified
□ Security requirements included
□ Monitoring approach defined

BUSINESS CHECKLIST:
□ ROI calculated
□ Success metrics defined
□ Stakeholders identified
□ Risks documented
□ Compliance verified

TESTING CHECKLIST:
□ Test data available
□ Test scenarios defined
□ Acceptance criteria clear
□ Edge cases documented
□ Rollback plan exists

Common Pitfalls to Avoid

❌ Vague Requirements

"System should be fast"

"Model should be accurate"

"Should handle large data"

✅ Specific Requirements

"Response time < 200ms for 95% of requests"

"Achieve 92% accuracy on test set"

"Process 1TB daily with 10K TPS"