Technical Implementation Guide: Prompt Repetition
For CODITECT Engineering Team
Architecture Overview
┌─────────────────────────────────────────────────────────────┐
│ CODITECT Request Pipeline │
├─────────────────────────────────────────────────────────────┤
│ │
│ Raw Request → [Prompt Optimizer] → LLM → Response │
│ ↓ │
│ Complexity Analysis │
│ ↓ │
│ ┌─────────┴─────────┐ │
│ │ Repetition Logic │ │
│ └─────────┬─────────┘ │
│ ↓ │
│ ┌──────────────┼──────────────┐ │
│ │ │ │ │
│ 1x (simple) 2x (standard) 3x (complex) │
│ │
└─────────────────────────────────────────────────────────────┘
Core Implementation
1. Prompt Repetition Strategy Class
from dataclasses import dataclass
from typing import Optional, Dict, List, Literal
import re
from enum import Enum
class RepetitionStrategy(Enum):
"""Repetition approach variants"""
SIMPLE = "simple" # <QUERY><QUERY>
VERBOSE = "verbose" # <QUERY> Let me repeat that: <QUERY>
TRIPLE = "triple" # 3x repetition for high complexity
ADAPTIVE = "adaptive" # Auto-select based on complexity
@dataclass
class ComplexityMetrics:
"""Task complexity indicators"""
length: int
has_list: bool
has_ordering: bool
has_multiple_choice: bool
options_first: bool
math_content: bool
def calculate_score(self) -> float:
"""Calculate 0-1 complexity score"""
score = 0.0
# Length contribution (capped at 0.15)
score += min(0.15, self.length / 5000 * 0.15)
# Feature contributions
if self.has_list:
score += 0.25
if self.has_ordering:
score += 0.30
if self.has_multiple_choice:
score += 0.20
if self.options_first:
score += 0.35 # High impact
if self.math_content:
score += 0.15
return min(1.0, score)
class PromptRepetitionOptimizer:
"""
Production-grade prompt repetition implementation
Features:
- Adaptive repetition based on complexity
- Multiple repetition strategies
- Cost-awareness and gating
- A/B testing support
- Metrics collection
"""
# Complexity thresholds
SIMPLE_THRESHOLD = 0.25 # Below this: no repetition
STANDARD_THRESHOLD = 0.60 # Above this: 2x repetition
COMPLEX_THRESHOLD = 0.75 # Above this: 3x repetition
# Cost gates
MAX_INPUT_TOKENS_2X = 10_000 # Max tokens for 2x
MAX_INPUT_TOKENS_3X = 5_000 # Max tokens for 3x
def __init__(
self,
strategy: RepetitionStrategy = RepetitionStrategy.ADAPTIVE,
cost_aware: bool = True,
ab_test_enabled: bool = False,
ab_test_baseline_pct: float = 0.10
):
self.strategy = strategy
self.cost_aware = cost_aware
self.ab_test_enabled = ab_test_enabled
self.ab_test_baseline_pct = ab_test_baseline_pct
# Metrics tracking
self.metrics: Dict[str, List[float]] = {
'complexity_scores': [],
'repetition_counts': [],
'token_costs': []
}
def optimize_prompt(
self,
prompt: str,
force_repetitions: Optional[int] = None,
task_type: Optional[str] = None
) -> Dict[str, any]:
"""
Optimize prompt with repetition
Returns:
{
'optimized_prompt': str,
'repetitions': int,
'complexity': float,
'strategy': str,
'variant': str, # 'baseline' or 'optimized'
'metadata': dict
}
"""
# A/B testing logic
if self.ab_test_enabled and force_repetitions is None:
import random
if random.random() < self.ab_test_baseline_pct:
return self._create_result(
prompt, 1, 0.0, 'baseline', task_type
)
# Analyze complexity
complexity = self._analyze_complexity(prompt)
# Determine repetition count
if force_repetitions:
reps = force_repetitions
else:
reps = self._get_repetition_count(prompt, complexity)
# Apply cost gates
if self.cost_aware:
reps = self._apply_cost_gates(prompt, reps)
# Generate optimized prompt
if reps == 1:
optimized = prompt
elif self.strategy == RepetitionStrategy.SIMPLE:
optimized = self._simple_repetition(prompt, reps)
elif self.strategy == RepetitionStrategy.VERBOSE:
optimized = self._verbose_repetition(prompt, reps)
elif self.strategy == RepetitionStrategy.TRIPLE:
optimized = self._verbose_repetition(prompt, 3)
else: # ADAPTIVE
if reps == 3 or complexity > self.COMPLEX_THRESHOLD:
optimized = self._verbose_repetition(prompt, reps)
else:
optimized = self._simple_repetition(prompt, reps)
return self._create_result(
optimized, reps, complexity,
self.strategy.value, task_type
)
def _analyze_complexity(self, prompt: str) -> float:
"""Analyze prompt complexity"""
metrics = ComplexityMetrics(
length=len(prompt),
has_list=self._detect_list(prompt),
has_ordering=self._detect_ordering(prompt),
has_multiple_choice=self._detect_multiple_choice(prompt),
options_first=self._detect_options_first(prompt),
math_content=self._detect_math(prompt)
)
score = metrics.calculate_score()
self.metrics['complexity_scores'].append(score)
return score
def _detect_list(self, prompt: str) -> bool:
"""Detect list processing tasks"""
patterns = [
r'\blist of\b',
r'\bhere(?:\'s| is) a list\b',
r'\bitems?:\s*\n',
r'\d+\.\s+\w+.*\n.*\d+\.\s+\w+' # Numbered list
]
return any(re.search(p, prompt, re.IGNORECASE) for p in patterns)
def _detect_ordering(self, prompt: str) -> bool:
"""Detect ordering/sequencing tasks"""
keywords = [
'between', 'order', 'sequence', 'before', 'after',
'first', 'last', 'middle', 'nth', 'position'
]
return any(kw in prompt.lower() for kw in keywords)
def _detect_multiple_choice(self, prompt: str) -> bool:
"""Detect multiple choice questions"""
# Look for A. B. C. D. pattern
pattern = r'[A-D]\.\s+\w+'
matches = re.findall(pattern, prompt)
return len(matches) >= 2
def _detect_options_first(self, prompt: str) -> bool:
"""Detect if options appear before question"""
if not self._detect_multiple_choice(prompt):
return False
# Find question mark position
question_pos = prompt.find('?')
if question_pos == -1:
return False
# Find first option position
option_match = re.search(r'[A-D]\.\s+', prompt)
if not option_match:
return False
option_pos = option_match.start()
# Options first if option appears before 70% of question
return option_pos < question_pos * 0.7
def _detect_math(self, prompt: str) -> bool:
"""Detect mathematical content"""
math_symbols = ['=', '+', '-', '×', '÷', '∑', '∫', '^']
return any(sym in prompt for sym in math_symbols)
def _get_repetition_count(
self,
prompt: str,
complexity: float
) -> int:
"""Determine optimal repetition count"""
if complexity < self.SIMPLE_THRESHOLD:
return 1
elif complexity < self.COMPLEX_THRESHOLD:
return 2
else:
return 3
def _apply_cost_gates(self, prompt: str, reps: int) -> int:
"""Apply cost-based limitations"""
# Estimate token count (rough: 1 token per 4 chars)
est_tokens = len(prompt) // 4
if reps >= 3 and est_tokens > self.MAX_INPUT_TOKENS_3X:
return 2 # Downgrade to 2x
elif reps >= 2 and est_tokens > self.MAX_INPUT_TOKENS_2X:
return 1 # Downgrade to 1x
return reps
def _simple_repetition(self, prompt: str, count: int) -> str:
"""Simple concatenation repetition"""
return (prompt + "\n\n") * count
def _verbose_repetition(self, prompt: str, count: int) -> str:
"""Verbose repetition with connectors"""
parts = [prompt]
for i in range(1, count):
if i == 1:
connector = "Let me repeat that:"
else:
connector = "Let me repeat that one more time:"
parts.extend([connector, prompt])
return "\n\n".join(parts)
def _create_result(
self,
prompt: str,
reps: int,
complexity: float,
strategy: str,
task_type: Optional[str]
) -> Dict:
"""Create result dictionary"""
self.metrics['repetition_counts'].append(reps)
return {
'optimized_prompt': prompt,
'repetitions': reps,
'complexity': complexity,
'strategy': strategy,
'variant': 'baseline' if reps == 1 else 'optimized',
'metadata': {
'task_type': task_type,
'original_length': len(prompt) // reps if reps > 1 else len(prompt),
'optimized_length': len(prompt),
'cost_multiplier': reps
}
}
def get_metrics_summary(self) -> Dict[str, float]:
"""Get summary statistics"""
if not self.metrics['complexity_scores']:
return {}
import statistics
return {
'avg_complexity': statistics.mean(self.metrics['complexity_scores']),
'avg_repetitions': statistics.mean(self.metrics['repetition_counts']),
'total_optimizations': len(self.metrics['complexity_scores']),
'pct_optimized': sum(1 for r in self.metrics['repetition_counts'] if r > 1) / len(self.metrics['repetition_counts']) * 100
}
2. CODITECT Integration Points
class CODITECTClassificationLayer:
"""Enhanced classification with prompt optimization"""
def __init__(self):
self.optimizer = PromptRepetitionOptimizer(
strategy=RepetitionStrategy.ADAPTIVE,
cost_aware=True,
ab_test_enabled=True,
ab_test_baseline_pct=0.10
)
self.llm_client = AnthropicClient()
self.metrics_collector = MetricsCollector()
async def classify_work_request(
self,
request: str,
available_agents: List[str]
) -> Dict[str, any]:
"""Classify work request with optimization"""
# Build classification prompt
base_prompt = self._build_classification_prompt(
request, available_agents
)
# Optimize prompt
optimization = self.optimizer.optimize_prompt(
base_prompt,
task_type='classification'
)
# Call LLM
start_time = time.time()
response = await self.llm_client.complete(
prompt=optimization['optimized_prompt'],
model='claude-sonnet-4-20250514'
)
latency = time.time() - start_time
# Parse response
agent_id = self._parse_classification(response)
# Collect metrics
await self.metrics_collector.record({
'variant': optimization['variant'],
'repetitions': optimization['repetitions'],
'complexity': optimization['complexity'],
'latency': latency,
'agent_id': agent_id,
'timestamp': time.time()
})
return {
'agent_id': agent_id,
'confidence': response.get('confidence', 0.0),
'optimization_metadata': optimization['metadata']
}
def _build_classification_prompt(
self,
request: str,
agents: List[str]
) -> str:
"""Build classification prompt"""
return f"""Analyze this work request and determine which agent should handle it:
Work Request:
{request}
Available Agents:
{chr(10).join(f'{i+1}. {agent["name"]}: {agent["description"]}' for i, agent in enumerate(agents))}
Reply with only the agent number (1-{len(agents)})."""
def _parse_classification(self, response: str) -> str:
"""Extract agent ID from response"""
# Simple regex-based extraction
match = re.search(r'\b(\d+)\b', response)
if match:
return f"agent_{match.group(1)}"
raise ValueError(f"Could not parse classification: {response}")
class CODITECTDependencyExtractor:
"""Enhanced dependency extraction with 3x repetition"""
def __init__(self):
# Force 3x repetition for complex dependency tasks
self.optimizer = PromptRepetitionOptimizer(
strategy=RepetitionStrategy.TRIPLE,
cost_aware=False # Always optimize regardless of cost
)
self.llm_client = AnthropicClient()
async def extract_dependencies(
self,
tasks: List[Dict[str, str]]
) -> List[Dict[str, any]]:
"""Extract task dependencies with optimization"""
# Build dependency prompt
base_prompt = self._build_dependency_prompt(tasks)
# Optimize with 3x repetition
optimization = self.optimizer.optimize_prompt(
base_prompt,
force_repetitions=3,
task_type='dependency_extraction'
)
# Call LLM
response = await self.llm_client.complete(
prompt=optimization['optimized_prompt'],
model='claude-sonnet-4-20250514'
)
# Parse dependencies
dependencies = self._parse_dependencies(response, tasks)
return dependencies
def _build_dependency_prompt(
self,
tasks: List[Dict[str, str]]
) -> str:
"""Build dependency extraction prompt"""
task_list = "\n".join(
f"{i+1}. {task['title']}: {task['description']}"
for i, task in enumerate(tasks)
)
return f"""Here's a list of tasks that need to be completed:
{task_list}
Analyze these tasks and identify which tasks depend on others.
A task depends on another if it cannot start until the other is complete.
Output format (one per line):
Task X depends on Task Y
If a task has no dependencies, don't list it."""
def _parse_dependencies(
self,
response: str,
tasks: List[Dict[str, str]]
) -> List[Dict[str, any]]:
"""Parse dependency relationships"""
dependencies = []
# Pattern: "Task X depends on Task Y"
pattern = r'Task (\d+) depends on Task (\d+)'
matches = re.finditer(pattern, response, re.IGNORECASE)
for match in matches:
dependent_idx = int(match.group(1)) - 1
prerequisite_idx = int(match.group(2)) - 1
if 0 <= dependent_idx < len(tasks) and 0 <= prerequisite_idx < len(tasks):
dependencies.append({
'dependent': tasks[dependent_idx]['id'],
'prerequisite': tasks[prerequisite_idx]['id'],
'confidence': 0.9 # High confidence with 3x repetition
})
return dependencies
3. Monitoring & Metrics
class PromptOptimizationMetrics:
"""Track prompt optimization performance"""
def __init__(self):
self.database = MetricsDatabase()
async def record_optimization(
self,
variant: str,
repetitions: int,
complexity: float,
latency: float,
accuracy: Optional[float] = None,
task_type: Optional[str] = None
):
"""Record optimization metrics"""
await self.database.insert({
'timestamp': time.time(),
'variant': variant,
'repetitions': repetitions,
'complexity': complexity,
'latency_ms': latency * 1000,
'accuracy': accuracy,
'task_type': task_type
})
async def generate_report(
self,
time_range: tuple
) -> Dict[str, any]:
"""Generate performance report"""
data = await self.database.query(time_range)
baseline = [d for d in data if d['variant'] == 'baseline']
optimized = [d for d in data if d['variant'] == 'optimized']
return {
'baseline': {
'count': len(baseline),
'avg_accuracy': self._avg(baseline, 'accuracy'),
'avg_latency': self._avg(baseline, 'latency_ms')
},
'optimized': {
'count': len(optimized),
'avg_accuracy': self._avg(optimized, 'accuracy'),
'avg_latency': self._avg(optimized, 'latency_ms'),
'avg_repetitions': self._avg(optimized, 'repetitions')
},
'improvement': {
'accuracy_lift': self._calculate_lift(
baseline, optimized, 'accuracy'
),
'latency_change': self._calculate_change(
baseline, optimized, 'latency_ms'
)
}
}
def _avg(self, data: List[Dict], key: str) -> float:
"""Calculate average for key"""
values = [d[key] for d in data if d.get(key) is not None]
return sum(values) / len(values) if values else 0.0
def _calculate_lift(
self,
baseline: List[Dict],
optimized: List[Dict],
key: str
) -> float:
"""Calculate percentage lift"""
base_avg = self._avg(baseline, key)
opt_avg = self._avg(optimized, key)
if base_avg == 0:
return 0.0
return ((opt_avg - base_avg) / base_avg) * 100
def _calculate_change(
self,
baseline: List[Dict],
optimized: List[Dict],
key: str
) -> float:
"""Calculate absolute change"""
base_avg = self._avg(baseline, key)
opt_avg = self._avg(optimized, key)
return opt_avg - base_avg
Deployment Plan
Phase 1: Classification Layer (Week 1)
scope:
- Implement PromptRepetitionOptimizer
- Integrate into classification endpoint
- Enable A/B testing (10% baseline, 90% optimized)
- Deploy metrics collection
verification:
- Unit tests for complexity detection
- Integration tests for classification
- Performance benchmarks
- A/B test statistical power calculation
rollout:
- Deploy to staging (Day 1-2)
- Internal testing (Day 3)
- Canary deployment 5% (Day 4)
- Full production rollout (Day 5-7)
success_metrics:
- Accuracy improvement: >10 percentage points
- Latency change: <10% increase
- Error rate: No increase
- Cost increase: 8-15% (acceptable)
Phase 2: Dependency Extraction (Week 2-3)
scope:
- Implement forced 3x repetition
- Integrate into workflow engine
- Add dependency confidence scoring
- Enhanced error handling
verification:
- Dependency accuracy testing
- Complex workflow scenarios
- Edge case handling
- Performance impact assessment
rollout:
- Feature flag controlled
- Beta customer testing
- Gradual rollout by customer tier
- Full deployment
success_metrics:
- Dependency accuracy: >90%
- False positive rate: <5%
- Workflow completion rate improvement: >20%
Phase 3: Document Analysis (Week 4-6)
scope:
- Adaptive repetition for long documents
- Section-based optimization
- Cost gating for very long inputs
- Batch processing optimization
verification:
- Various document types testing
- Length scaling tests
- Cost impact analysis
- Quality benchmarking
rollout:
- Document type segmentation
- Phased by document complexity
- Customer opt-in initially
- General availability
success_metrics:
- Action item extraction: >20% improvement
- Processing cost: <20% increase
- Customer satisfaction: Maintain or improve
Testing Strategy
Unit Tests
import pytest
from coditect.optimization import PromptRepetitionOptimizer, RepetitionStrategy
class TestPromptOptimization:
def setup_method(self):
self.optimizer = PromptRepetitionOptimizer(
strategy=RepetitionStrategy.ADAPTIVE,
cost_aware=True
)
def test_simple_prompt_no_repetition(self):
"""Simple prompts should not be repeated"""
prompt = "What is 2+2?"
result = self.optimizer.optimize_prompt(prompt)
assert result['repetitions'] == 1
assert result['complexity'] < 0.25
assert result['variant'] == 'baseline'
def test_list_processing_gets_repetition(self):
"""List processing tasks should get repetition"""
prompt = """Here's a list of names:
1. Alice
2. Bob
3. Carol
What is the 2nd name?"""
result = self.optimizer.optimize_prompt(prompt)
assert result['repetitions'] >= 2
assert result['complexity'] > 0.25
def test_options_first_high_complexity(self):
"""Options-first should be detected as high complexity"""
prompt = """A. Red
B. Blue
C. Green
Which color is in the sky?"""
result = self.optimizer.optimize_prompt(prompt)
assert result['repetitions'] >= 2
assert result['complexity'] > 0.6 # High complexity
def test_cost_gating_very_long_prompt(self):
"""Very long prompts should be cost-gated"""
# Create a prompt > 10,000 tokens
prompt = "Word " * 15000
result = self.optimizer.optimize_prompt(prompt)
# Should be downgraded from 2x to 1x
assert result['repetitions'] == 1
def test_forced_repetition_overrides(self):
"""Forced repetition should override complexity analysis"""
prompt = "Simple question?"
result = self.optimizer.optimize_prompt(
prompt,
force_repetitions=3
)
assert result['repetitions'] == 3
def test_verbose_repetition_format(self):
"""Verbose repetition should include connectors"""
optimizer = PromptRepetitionOptimizer(
strategy=RepetitionStrategy.VERBOSE
)
prompt = "Test question?"
result = optimizer.optimize_prompt(
prompt,
force_repetitions=2
)
assert "Let me repeat that:" in result['optimized_prompt']
assert result['optimized_prompt'].count(prompt) == 2
Integration Tests
@pytest.mark.integration
class TestClassificationIntegration:
@pytest.fixture
async def classifier(self):
return CODITECTClassificationLayer()
async def test_classification_accuracy_improvement(self, classifier):
"""Verify accuracy improvement in production"""
test_cases = load_test_cases('classification_benchmark.json')
results = []
for case in test_cases:
result = await classifier.classify_work_request(
case['request'],
case['agents']
)
results.append({
'predicted': result['agent_id'],
'expected': case['expected_agent'],
'variant': result['optimization_metadata']['variant']
})
# Calculate accuracy by variant
baseline_accuracy = self._calculate_accuracy(
[r for r in results if r['variant'] == 'baseline']
)
optimized_accuracy = self._calculate_accuracy(
[r for r in results if r['variant'] == 'optimized']
)
# Verify improvement
improvement = optimized_accuracy - baseline_accuracy
assert improvement > 0.10 # >10 percentage points
def _calculate_accuracy(self, results: List[Dict]) -> float:
if not results:
return 0.0
correct = sum(1 for r in results if r['predicted'] == r['expected'])
return correct / len(results)
Monitoring Dashboards
Key Metrics
# Grafana dashboard configuration
DASHBOARD_METRICS = {
'accuracy_by_variant': {
'query': """
SELECT
variant,
AVG(accuracy) as avg_accuracy,
COUNT(*) as sample_size
FROM optimization_metrics
WHERE timestamp > NOW() - INTERVAL '7 days'
GROUP BY variant
""",
'visualization': 'bar_chart'
},
'latency_distribution': {
'query': """
SELECT
variant,
PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY latency_ms) as p50,
PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY latency_ms) as p95,
PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY latency_ms) as p99
FROM optimization_metrics
WHERE timestamp > NOW() - INTERVAL '24 hours'
GROUP BY variant
""",
'visualization': 'line_chart'
},
'cost_impact': {
'query': """
SELECT
DATE_TRUNC('day', timestamp) as day,
SUM(CASE WHEN variant = 'optimized' THEN repetitions ELSE 1 END) as total_cost_units,
AVG(repetitions) as avg_repetitions
FROM optimization_metrics
WHERE timestamp > NOW() - INTERVAL '30 days'
GROUP BY day
ORDER BY day
""",
'visualization': 'area_chart'
},
'complexity_distribution': {
'query': """
SELECT
CASE
WHEN complexity < 0.25 THEN 'simple'
WHEN complexity < 0.60 THEN 'moderate'
WHEN complexity < 0.75 THEN 'complex'
ELSE 'very_complex'
END as category,
COUNT(*) as count,
AVG(repetitions) as avg_reps
FROM optimization_metrics
WHERE timestamp > NOW() - INTERVAL '7 days'
GROUP BY category
""",
'visualization': 'pie_chart'
}
}
Error Handling & Fallbacks
class PromptOptimizationError(Exception):
"""Base exception for optimization failures"""
pass
class TokenLimitExceeded(PromptOptimizationError):
"""Prompt exceeds token limits after repetition"""
pass
class SafePromptOptimizer:
"""Fail-safe optimizer with automatic fallbacks"""
def __init__(self):
self.primary = PromptRepetitionOptimizer(
strategy=RepetitionStrategy.ADAPTIVE
)
self.fallback = PromptRepetitionOptimizer(
strategy=RepetitionStrategy.SIMPLE,
cost_aware=True
)
self.error_tracker = ErrorTracker()
async def optimize_with_fallback(
self,
prompt: str,
**kwargs
) -> Dict[str, any]:
"""Optimize with automatic fallback on error"""
try:
# Try primary optimizer
result = self.primary.optimize_prompt(prompt, **kwargs)
# Verify result
if len(result['optimized_prompt']) > 100_000: # Token limit
raise TokenLimitExceeded(
f"Prompt too long: {len(result['optimized_prompt'])}"
)
return result
except TokenLimitExceeded as e:
# Fallback to non-repeated prompt
await self.error_tracker.log({
'error': str(e),
'fallback': 'no_repetition'
})
return {
'optimized_prompt': prompt,
'repetitions': 1,
'complexity': 0.0,
'strategy': 'fallback',
'variant': 'baseline',
'metadata': {'fallback_reason': str(e)}
}
except Exception as e:
# Unexpected error - log and return baseline
await self.error_tracker.log({
'error': str(e),
'fallback': 'unexpected_error'
})
return {
'optimized_prompt': prompt,
'repetitions': 1,
'complexity': 0.0,
'strategy': 'error_fallback',
'variant': 'baseline',
'metadata': {'error': str(e)}
}
Performance Benchmarks
Target metrics for production deployment:
| Metric | Baseline | Target | Maximum |
|---|---|---|---|
| Classification accuracy | 85% | 95% | - |
| Dependency accuracy | 65% | 90% | - |
| P50 latency | 500ms | 550ms | 750ms |
| P95 latency | 1200ms | 1400ms | 2000ms |
| Cost increase | - | 8-15% | 25% |
| Error rate | 0.1% | 0.1% | 0.5% |
Rollback Plan
rollback_triggers:
- accuracy_degradation: >5 percentage points
- latency_increase: >50%
- error_rate_spike: >0.5%
- cost_overrun: >30%
- customer_complaints: >10 per day
rollback_procedure:
1. Disable optimization via feature flag
2. Verify metrics return to baseline
3. Analyze logs for root cause
4. Create incident report
5. Plan remediation
6. Re-deploy with fixes
feature_flags:
- coditect.optimization.classification.enabled
- coditect.optimization.dependencies.enabled
- coditect.optimization.documents.enabled
- coditect.optimization.ab_test_pct
Summary
Implementation Complexity: Low
Deployment Risk: Very Low
Expected ROI: 300-500x
Timeline: 1 week to production (classification)
Maintenance: Minimal
Go/No-Go: ✅ GO - All signals positive for immediate implementation.
Next Steps:
- Review this guide with engineering team
- Set up A/B testing infrastructure
- Implement Phase 1 (classification layer)
- Monitor for 1 week
- Proceed to Phase 2 if metrics validate