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Week 1: Foundation and First API Calls
The goal of week 1 is to prove the concept works at all. Don't optimize—validate.
Day 1-2: Define the Use Case
- • Identify ONE user workflow to augment (not replace)
- • Write 10 example inputs you expect users to provide
- • Write the ideal outputs for each input
- • This becomes your first evaluation dataset
Day 3-4: API Integration
- • Set up OpenAI/Anthropic API access
- • Create a simple wrapper class with retry logic
- • Add logging for prompts, responses, latency, and costs
- • Test with your 10 examples from Day 1-2
Day 5: First Iteration
- • Review outputs from Day 3-4 testing
- • Iterate on prompt based on failures
- • Document what works and what doesn't
- • Decision: Is this approach viable?
Week 1 Deliverable
Working API integration with 80%+ success rate on your 10 test cases.
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Week 2: Product Integration
Week 2 connects your AI to the product. Focus on the happy path—don't handle every edge case yet.
Day 6-7: Backend Integration
- • Create an API endpoint for your AI feature
- • Connect to user context (who's asking, what data they have)
- • Implement basic rate limiting
- • Add error handling for API failures
Day 8-9: Frontend UI
- • Build minimal UI (input, loading state, output)
- • Implement streaming for long responses
- • Add basic error states
- • Keep it simple—this is throwaway UI
Day 10: Internal Testing
- • Deploy to staging
- • Have team members try the feature
- • Collect feedback and bugs
- • Prioritize fixes for week 3
Week 2 Deliverable
End-to-end flow working in staging. Team can use the feature.
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Week 3: Beta Users and Evaluation
Week 3 is about getting real user data. Deploy to a small group and measure everything.
Day 11-12: Evaluation Infrastructure
- • Add thumbs up/down feedback UI
- • Log all user interactions to a database
- • Create a dashboard for success rate metrics
- • Set up alerts for failures and errors
Day 13-14: Beta Rollout
- • Select 5-10 beta users (power users who give feedback)
- • Deploy with feature flag for controlled access
- • Proactively reach out for feedback
- • Monitor metrics closely
Day 15: Iteration
- • Review all feedback and failure cases
- • Iterate on prompts based on real failures
- • Fix the top 3 bugs from beta
- • Expand golden dataset with real examples
Week 3 Deliverable
Real usage data from 5-10 users. Success rate measured. Top issues identified.
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Week 4: Production Hardening
Week 4 prepares for broader rollout. Add the production patterns that make AI reliable.
Day 16-17: Production Patterns
- • Add graceful degradation (fallback when AI fails)
- • Implement proper rate limiting per user
- • Add timeout handling with retries
- • Implement cost tracking and alerts
Day 18-19: Polish and Documentation
- • Polish the UI based on beta feedback
- • Add user-facing documentation/tooltips
- • Document the system for your team
- • Create runbook for common issues
Day 20: Launch
- • Gradual rollout (25% → 50% → 100%)
- • Monitor metrics as you expand
- • Be ready to roll back if needed
- • Celebrate shipping real AI to production 🎉
Week 4 Deliverable
AI feature live in production for all users. Monitoring and fallbacks in place.