Implementation Roadmap: Prompt Repetition for CODITECT

90-Day Deployment Plan

Overview

Objective: Deploy prompt repetition optimization across CODITECT platform to achieve 95%+ classification accuracy and 90%+ dependency accuracy.

Timeline: 12 weeks (3 months)
Resources: 1.5 FTE engineering, 0.5 FTE product, 0.25 FTE QA
Budget: $105,000
Expected ROI: 300-500x
Risk Level: Very Low

Phase Structure

Week 1-2: Foundation & Classification
Week 3-4: Dependency Extraction  
Week 5-6: Document Analysis
Week 7-8: Optimization & Scale
Week 9-10: Advanced Features
Week 11-12: Productization & Marketing

Week-by-Week Breakdown

Week 1: Foundation (Days 1-7)

Engineering Tasks

Day 1-2: Core Implementation

• Create PromptRepetitionOptimizer class
• Implement complexity detection algorithms
• Build repetition strategy variants (simple, verbose, 3x)
• Add cost gating logic
• Write unit tests (>90% coverage)

Deliverables:

• coditect/optimization/prompt_optimizer.py
• coditect/optimization/complexity_detector.py
• tests/test_prompt_optimization.py

Success Criteria:

• All unit tests passing
• Complexity detection accuracy >85%
• Cost gates functioning correctly

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Day 3-4: Integration Layer

• Integrate optimizer into classification pipeline
• Add metrics collection hooks
• Implement A/B testing infrastructure (10/90 split)
• Create monitoring dashboards
• Set up logging

Deliverables:

• coditect/classification/optimized_classifier.py
• coditect/metrics/optimization_tracker.py
• Grafana dashboard configurations

Success Criteria:

• Classification endpoint accepting optimized prompts
• Metrics flowing to dashboard
• A/B test randomization working

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Day 5: Testing & Validation

• Integration tests with production-like data
• Performance benchmarking
• Latency testing
• Cost estimation validation
• Edge case testing

Deliverables:

• Integration test suite
• Performance benchmark report
• Cost projection model

Success Criteria:

• All integration tests passing
• Latency <10% increase
• Cost estimates within ±5% of actual

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Day 6-7: Staging Deployment

• Deploy to staging environment
• Run staging smoke tests
• Verify metrics collection
• Test rollback procedures
• Documentation updates

Deliverables:

• Staging deployment
• Runbook for operations
• Rollback scripts

Success Criteria:

• Staging tests all passing
• Metrics visible in dashboards
• Rollback tested and working

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Week 1 Deliverables Summary

Deliverable	Owner	Status	Risk
Core optimizer implementation	Engineering	⏳	Low
Integration with classification	Engineering	⏳	Low
A/B testing infrastructure	Engineering	⏳	Low
Metrics dashboards	Engineering	⏳	Low
Staging deployment	DevOps	⏳	Low

Week 1 Gate Criteria

Go/No-Go for Week 2:

• ✓ All unit tests passing
• ✓ Staging deployment successful
• ✓ Metrics collection functional
• ✓ Performance within acceptable ranges

If No-Go: Extend Week 1 by 2-3 days, reassess.

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Week 2: Production Rollout - Classification (Days 8-14)

Engineering Tasks

Day 8-9: Canary Deployment

• Deploy to 5% of production traffic
• Monitor for errors
• Collect initial accuracy metrics
• Compare baseline vs optimized
• Adjust parameters if needed

Monitoring Checklist:

• Error rate < 0.1%
• Latency P95 < 1.5x baseline
• Accuracy improvement visible
• Cost increase within budget
• No customer complaints

Success Criteria:

• Accuracy improvement >5 percentage points
• No degradation in any metric
• Zero critical errors

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Day 10-11: Gradual Rollout

• Increase to 25% of traffic
• Increase to 50% of traffic
• Increase to 75% of traffic
• Continue monitoring

Rollout Gates:

• 6 hours stable at each percentage
• No errors or degradation
• Metrics improving as expected

---

Day 12-14: Full Production

• Roll out to 100% of traffic
• 48-hour burn-in period
• Collect comprehensive metrics
• Generate Week 2 report
• Stakeholder update

Week 2 Report Contents:

1. Accuracy improvement (baseline vs optimized)
2. Cost impact (actual vs estimated)
3. Latency analysis
4. Error rate comparison
5. Customer impact (support tickets)
6. Recommendations for Phase 2

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Week 2 Deliverables Summary

Metric	Target	Actual	Status
Classification accuracy	>92%	TBD	⏳
Accuracy improvement	>10pp	TBD	⏳
Latency P95	<750ms	TBD	⏳
Error rate	<0.1%	TBD	⏳
Cost increase	<15%	TBD	⏳

Week 2 Gate Criteria

Go/No-Go for Week 3:

• ✓ Accuracy improvement >8 percentage points (80% of target)
• ✓ No critical production issues
• ✓ Cost within budget
• ✓ Customer satisfaction maintained

If No-Go: Rollback to baseline, analyze issues, extend timeline by 1 week.

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Week 3-4: Dependency Extraction (Days 15-28)

Engineering Tasks

Day 15-18: Implementation

• Implement 3x repetition for dependencies
• Integrate into workflow engine
• Add dependency confidence scoring
• Build dependency validation logic
• Write tests

Deliverables:

• coditect/workflows/dependency_extractor_v2.py
• Enhanced test suite
• Confidence scoring model

---

Day 19-21: Testing

• Test on historical dependency data
• Validate accuracy improvements
• Test complex workflow scenarios
• Edge case handling
• Performance testing

Test Scenarios:

1. Simple linear dependencies (A→B→C)
2. Fan-out (A→B,C,D)
3. Fan-in (A,B,C→D)
4. Complex graphs (cycles, multiple paths)
5. Missing dependencies
6. Ambiguous relationships

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Day 22-28: Deployment

• Feature flag rollout
• Beta customer testing (3-5 customers)
• Collect feedback
• Iterate based on results
• Full production rollout

Beta Customer Selection:

• Customer A: High complexity workflows
• Customer B: High volume
• Customer C: Representative mid-market

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Week 3-4 Deliverables Summary

Metric	Target	Actual	Status
Dependency accuracy	>90%	TBD	⏳
False positive rate	<5%	TBD	⏳
Workflow completion	+20%	TBD	⏳
Beta customer satisfaction	>8/10	TBD	⏳

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Week 5-6: Document Analysis (Days 29-42)

Engineering Tasks

Day 29-33: Implementation

• Adaptive repetition for documents
• Section-based optimization
• Cost gating for long documents
• Batch processing optimization
• Action item extraction enhancement

Document Type Support:

• Meeting notes
• Email threads
• Requirements documents
• Technical specifications
• Customer feedback

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Day 34-38: Testing & Optimization

• Test various document types
• Length scaling tests (100-10,000 tokens)
• Cost/accuracy tradeoff analysis
• Quality benchmarking
• Comparative analysis vs baseline

---

Day 39-42: Rollout

• Document type segmentation
• Phased rollout by complexity
• Customer opt-in program
• Feedback collection
• General availability

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Week 7-8: Optimization & Scale (Days 43-56)

Engineering Tasks

Performance Optimization:

• KV-cache optimization (keep only 2nd repetition)
• Prefill parallelization tuning
• Batch processing improvements
• Memory optimization
• Cost reduction strategies

Monitoring & Observability:

• Enhanced metrics dashboards
• Alerting setup
• Performance profiling
• Cost tracking tools
• Anomaly detection

Scale Testing:

• Load testing (10x typical volume)
• Stress testing
• Failover testing
• Multi-region deployment
• Disaster recovery validation

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Week 9-10: Advanced Features (Days 57-70)

Engineering Tasks

Adaptive Intelligence:

• ML-based complexity prediction
• Dynamic repetition count optimization
• Context-aware variant selection
• Customer-specific tuning
• Continuous learning pipeline

Integration Enhancements:

• API endpoints for external access
• Webhook support
• Custom optimization rules
• White-label configurations
• Partner integrations

Advanced Monitoring:

• Predictive analytics
• Accuracy forecasting
• Cost optimization recommendations
• Customer-specific dashboards
• Executive reporting

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Week 11-12: Productization & Marketing (Days 71-84)

Product Tasks

Feature Packaging:

• "Accuracy Plus" tier definition
• SLA development (95%+ accuracy guarantee)
• Pricing model finalization
• Customer migration plan
• Documentation updates

Customer Communication:

• Release notes
• Customer webinar
• Case study production (3-5)
• Video testimonials
• FAQ documentation

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Marketing Tasks

Content Creation:

• Technical blog post
• Press release
• Analyst briefings
• Social media campaign
• SEO optimization

Sales Enablement:

• Sales training (2 sessions)
• Demo environment updates
• ROI calculator deployment
• Competitive battle cards
• One-pagers and collateral

Launch Activities:

• Product launch event
• Customer communications
• Partner announcements
• Industry publication outreach
• Conference presentations

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Resource Allocation

Engineering (1.5 FTE)

Week	Senior Engineer (1.0 FTE)	Mid-Level Engineer (0.5 FTE)
1-2	Core implementation, integration	Testing, metrics
3-4	Dependency extraction	Testing, documentation
5-6	Document analysis	Optimization
7-8	Performance optimization	Monitoring
9-10	Advanced features	API development
11-12	Production support	Documentation

Total Engineering Cost: $75,000

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Product Management (0.5 FTE)

Week	Activities
1-2	Requirements, specs, gate criteria
3-4	Beta coordination, feedback analysis
5-6	Document strategy, customer testing
7-8	Roadmap updates, metrics review
9-10	Feature packaging, pricing
11-12	Launch coordination, customer comms

Total PM Cost: $20,000

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QA (0.25 FTE)

Week	Activities
1-2	Test plan development, automation
3-4	Regression testing, validation
5-6	Document testing, edge cases
7-8	Load testing, performance validation
9-10	Integration testing
11-12	Release validation

Total QA Cost: $10,000

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Budget Breakdown

Category	Amount	Timing
Engineering	$75,000	Weeks 1-12
Product Management	$20,000	Weeks 1-12
QA	$10,000	Weeks 1-12
Infrastructure	$2,000	One-time
Marketing	$5,000	Weeks 11-12
Contingency (10%)	$11,200	As needed
Total	$123,200	-

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Risk Management

Technical Risks

Risk	Probability	Impact	Mitigation
Integration issues	15%	Medium	Extensive testing, rollback plan
Performance degradation	10%	Medium	Performance gates, optimization
Token cost overrun	20%	Low	Cost gates, monitoring
Accuracy below target	10%	High	A/B testing, iteration
Production incidents	5%	High	Gradual rollout, monitoring

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Business Risks

Risk	Probability	Impact	Mitigation
Customer confusion	15%	Low	Clear communication, transparency
Delayed market adoption	20%	Medium	Strong sales enablement
Competitor response	50%	Medium	Fast execution, first-mover advantage
Lower than expected ROI	10%	Medium	Conservative projections, validation

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Mitigation Strategies

Technical Mitigation:

1. Comprehensive testing at each phase
2. Gradual rollout with monitoring
3. Rollback procedures ready
4. Performance gates at each stage
5. Continuous monitoring and alerting

Business Mitigation:

1. Early customer communication
2. Beta program for feedback
3. Clear value proposition messaging
4. ROI guarantees for new customers
5. Executive sponsorship

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Success Metrics

Technical Metrics

Metric	Baseline	Target	Measurement
Classification accuracy	85%	95%	Weekly
Dependency accuracy	65%	90%	Weekly
Latency P95	650ms	<750ms	Daily
Error rate	0.08%	<0.1%	Daily
Cost increase	-	<15%	Weekly
Uptime	99.9%	>99.9%	Daily

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Business Metrics

Metric	Baseline	Target	Measurement
Customer NPS	42	50	Monthly
Support tickets (accuracy)	100/week	<60/week	Weekly
Win rate	32%	40%	Monthly
Churn (accuracy-related)	8%	<5%	Quarterly
Average deal size	$45K	$50K	Monthly
Customer satisfaction	72%	>85%	Monthly

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Marketing Metrics

Metric	Baseline	Target	Measurement
Accuracy mentions in demos	15%	80%	Weekly
Case studies published	0	5	End of Q
Press mentions	0	10	End of Q
Sales collateral created	0	15	End of Q
Customer testimonials	0	8	End of Q

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Go/No-Go Gates

Phase 1 Gate (End of Week 2)

Required for Phase 2:

• ✓ Classification accuracy >92%
• ✓ Accuracy improvement >8pp
• ✓ No critical production issues
• ✓ Cost within budget (+15% max)
• ✓ Latency acceptable (<10% increase)

Decision: Proceed to Phase 2 / Pause / Rollback

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Phase 2 Gate (End of Week 4)

Required for Phase 3:

• ✓ Dependency accuracy >85%
• ✓ False positive rate <10%
• ✓ Beta customer satisfaction >7/10
• ✓ No show-stopping issues
• ✓ Workflow completion improvement >15%

Decision: Proceed to Phase 3 / Pause / Iterate

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Phase 3 Gate (End of Week 6)

Required for Phase 4:

• ✓ Document accuracy improvement >15%
• ✓ All subsystems deployed successfully
• ✓ Customer feedback positive
• ✓ Technical debt manageable
• ✓ Cost within overall budget

Decision: Proceed to Phase 4 / Pause

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Communication Plan

Internal Stakeholders

Weekly Updates (All stakeholders):

• Engineering progress
• Metrics summary
• Risks and issues
• Next week plan

Bi-Weekly Demos (Leadership):

• Live demo of new capabilities
• Metrics review
• ROI tracking
• Competitive positioning

Monthly Business Reviews (Executive):

• Strategic impact
• Financial performance
• Market response
• Customer feedback

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External Communication

Week 2: Internal announcement - "CODITECT accuracy improvements"
Week 6: Customer newsletter - "New accuracy features available"
Week 12: Public launch - Press release, blog post, social media

Customer Communication:

• Release notes (weekly)
• Feature announcements (bi-weekly)
• Webinar (end of project)
• Case studies (ongoing)

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Rollback Procedures

Trigger Conditions

Automatic rollback if:

• Error rate >0.5% for >1 hour
• Latency P95 >2x baseline for >30 min
• Customer complaints >10 in 1 day
• Accuracy degradation >5pp

Manual rollback if:

• Unforeseen technical issues
• Business decision (e.g., cost overrun)
• Critical bug discovered

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Rollback Steps

1. Immediate (0-5 minutes):

   • Disable feature flag
   • Revert to baseline prompts
   • Alert on-call engineer

2. Short-term (5-30 minutes):

   • Verify systems restored
   • Assess impact
   • Customer communication if needed

3. Analysis (1-4 hours):

   • Root cause analysis
   • Impact assessment
   • Fix development plan

4. Resolution (1-7 days):

   • Implement fixes
   • Re-test thoroughly
   • Plan re-deployment

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Post-Launch Activities

Month 2 (Weeks 13-16)

Optimization:

• Fine-tune complexity thresholds
• Optimize cost gates
• Performance improvements
• Customer-specific tuning

Expansion:

• Additional use cases
• New customer tiers
• Partner integrations
• API enhancements

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Month 3 (Weeks 17-20)

Advanced Features:

• Predictive optimization
• Multi-modal support
• Enhanced analytics
• Custom rules engine

Market Expansion:

• New verticals
• Enterprise features
• International markets
• Channel partnerships

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Success Criteria Summary

Must-Have (30 Days)

• Classification accuracy >92%
• A/B test shows >8pp improvement
• Customer complaints -20%
• Token costs <20% increase
• Zero critical production issues

Should-Have (60 Days)

• All subsystems optimized
• Support tickets -35%
• Customer NPS +4 points
• 2 customer case studies
• Sales team trained

Could-Have (90 Days)

• Win rate +8pp
• Customer retention +2pp
• 5 press mentions
• Premium tier launched
• Partner integrations live

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Appendix

A. Testing Checklist

Unit Tests:

• Complexity detection
• Repetition logic
• Cost gates
• Edge cases
• Error handling

Integration Tests:

• Classification pipeline
• Dependency extraction
• Document analysis
• Metrics collection
• A/B testing

Performance Tests:

• Load testing
• Stress testing
• Latency profiling
• Memory profiling
• Cost validation

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B. Monitoring Dashboards

Real-time Monitoring:

1. Accuracy by variant (baseline vs optimized)
2. Latency distribution (P50, P95, P99)
3. Error rates
4. Token costs
5. Request volume

Business Metrics:

1. Customer satisfaction scores
2. Support ticket trends
3. Win/loss rates
4. Churn indicators
5. Revenue impact

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C. Escalation Paths

Severity Levels:

P0 (Critical):

• Production down
• Accuracy degradation >10pp
• Security breach
• Escalate to: VP Engineering, CTO

P1 (High):

• Feature not working
• Accuracy degradation 5-10pp
• Multiple customer complaints
• Escalate to: Engineering Manager, Product Lead

P2 (Medium):

• Performance issues
• Minor accuracy degradation <5pp
• Single customer complaint
• Escalate to: Team Lead

P3 (Low):

• Enhancement requests
• Documentation issues
• Escalate to: Product backlog

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Project Sponsor: [VP Product]
Technical Lead: [Senior Engineer]
Product Owner: [Product Manager]
Last Updated: January 2026
Next Review: Weekly during implementation