Multi-Step Reasoning: Teaching AI to Think

"Build a user authentication system"
→ [Single step generation]
→ Basic but potentially flawed result

"Let's build a user authentication system step by step:
1. First, analyze the security requirements
2. Then, design the database schema
3. Next, implement the authentication flow
4. After that, add security measures
5. Finally, create comprehensive tests"
→ [Systematic, verified solution]

I need to optimize this slow database query. Let me think through this step by step:

1. First, I'll analyze what the query is doing:
   - It's joining 4 tables
   - Filtering on non-indexed columns
   - Returning large result sets

2. Next, I'll identify the bottlenecks:
   - Missing indexes on WHERE clauses
   - N+1 query problems in the joins
   - Unnecessary data being selected

3. Then I'll design optimizations:
   - Add composite indexes for common filters
   - Implement query batching
   - Use projection to limit columns

4. Finally, I'll implement and measure:
   - Create optimized query version
   - Add performance monitoring
   - Compare before/after metrics

Now implement this optimization strategy:

Create a real-time chat system with message encryption. 
Let's think step by step:

[AI will automatically break down the problem and solve systematically]

Example 1 - Building a payment system:
Step 1: Analyze payment requirements (PCI compliance, multiple providers)
Step 2: Design secure API endpoints with proper validation
Step 3: Implement transaction handling with rollback capability
Step 4: Add comprehensive error handling and logging
Step 5: Create thorough testing including edge cases

Example 2 - Building a caching system:
Step 1: Identify what needs to be cached (database queries, API responses)
Step 2: Choose appropriate caching strategies (TTL, LRU, write-through)
Step 3: Implement cache layer with proper key management
Step 4: Add cache invalidation and consistency mechanisms
Step 5: Monitor cache performance and hit rates

Now apply this same systematic approach to build a file upload system:

I need to build a complex e-commerce platform. Let me decompose this systematically:

LEVEL 1 DECOMPOSITION (Main Components):
- User Management System
- Product Catalog System  
- Shopping Cart System
- Payment Processing System
- Order Management System
- Inventory Management System

LEVEL 2 DECOMPOSITION (User Management):
- Registration and Authentication
- User Profiles and Preferences
- Password Reset and Security
- Account Management and Settings

LEVEL 3 DECOMPOSITION (Registration):
- Input validation and sanitization
- Email verification workflow
- Password strength enforcement
- Account creation and storage
- Welcome email automation

Now let's implement each Level 3 component systematically:

Build a microservices architecture with proper service communication.

DEPENDENCY ANALYSIS:
Prerequisites needed first:
1. Service discovery mechanism (must be first)
2. Configuration management system
3. Logging and monitoring infrastructure
4. Authentication/authorization system

Then we can build:
5. Individual microservices (depends on 1-4)
6. API Gateway (depends on 1,4,5)
7. Inter-service communication (depends on 1,5)
8. Health checking and circuit breakers (depends on 5,7)

IMPLEMENTATION ORDER:
Let's start with #1 (service discovery) since everything depends on it:

Create a recommendation engine using iterative development:

ITERATION 1 (MVP - Basic functionality):
Goal: Simple content-based recommendations
Steps:
1. Implement basic user preference tracking
2. Create simple similarity algorithms  
3. Generate basic recommendations
4. Test with small dataset

ITERATION 2 (Enhanced - Collaborative filtering):
Goal: Add collaborative filtering
Dependencies: User interaction data from Iteration 1
Steps:
1. Analyze user behavior patterns from V1
2. Implement collaborative filtering algorithm
3. Combine content-based + collaborative approaches
4. A/B test against V1 performance

ITERATION 3 (Advanced - ML-powered):
Goal: Machine learning recommendations
Dependencies: Large dataset from Iterations 1-2
Steps:
1. Prepare training data from previous iterations
2. Implement ML model training pipeline
3. Add real-time model serving
4. Implement feedback loop for continuous learning

Let's start with Iteration 1:

Create a user authentication API with built-in verification.

IMPLEMENTATION:
[Generate the authentication code]

VERIFICATION CHECKLIST:
Now let me verify this implementation step by step:

✅ Security Check:
- Are passwords properly hashed? 
- Is rate limiting implemented?
- Are JWT tokens properly signed?
- Is input validation comprehensive?

✅ Functionality Check:
- Does registration create valid user accounts?
- Does login return proper tokens?
- Does logout invalidate sessions?
- Do password resets work securely?

✅ Edge Cases Check:
- What happens with duplicate emails?
- How are malformed requests handled?
- Is concurrent access properly managed?
- Are database errors handled gracefully?

VERIFICATION RESULTS:
[AI reviews its own code and reports issues]

CORRECTIONS NEEDED:
[AI fixes any identified problems]

Design a file storage system considering multiple perspectives:

DEVELOPER PERSPECTIVE:
- Easy-to-use API
- Good error handling
- Comprehensive documentation
- Efficient performance

SECURITY PERSPECTIVE:
- File type validation
- Access control and permissions
- Virus scanning integration
- Secure file deletion

USER PERSPECTIVE:
- Fast upload/download speeds
- Progress indicators
- File organization features
- Mobile compatibility

OPERATIONS PERSPECTIVE:
- Monitoring and alerting
- Backup and recovery
- Scalability planning
- Cost optimization

INTEGRATION ANALYSIS:
Now let's find the optimal solution that satisfies all perspectives:
[Synthesize requirements from all viewpoints]

Build a payment processing system using error-first design:

ERROR ANALYSIS FIRST:
What could go wrong?
1. Network timeouts during payment processing
2. Payment provider API failures
3. Database transaction rollback needs
4. Double-charging scenarios
5. Insufficient funds handling
6. Fraud detection false positives

FAILURE MODE PLANNING:
For each error scenario:
1. Network timeouts → Implement retry with exponential backoff
2. API failures → Circuit breaker pattern with fallback providers
3. DB issues → Distributed transaction management
4. Double-charging → Idempotency keys and duplicate detection
5. Insufficient funds → Graceful error handling and user notification
6. Fraud alerts → Manual review queue with automated rules

ROBUST IMPLEMENTATION:
Now build the payment system with these failure modes addressed:

Build a complete DevOps pipeline with systematic reasoning:

TOP-LEVEL GOAL: Automated CI/CD pipeline with monitoring

LEVEL 1 BREAKDOWN:
A. Source Code Management
B. Continuous Integration  
C. Continuous Deployment
D. Monitoring and Alerting

LEVEL 2 BREAKDOWN:
A. Source Code Management:
   A1. Git workflow and branching strategy
   A2. Code review and quality gates
   A3. Security scanning integration

B. Continuous Integration:
   B1. Automated testing pipeline
   B2. Build and artifact generation
   B3. Quality and security checks

[Continue breaking down each component...]

IMPLEMENTATION SEQUENCE:
Start with A1 (Git workflow) as foundation:

Design a high-performance API with specific constraints:

CONSTRAINTS:
- Must handle 10,000 requests/second
- Response time < 100ms for 95% of requests
- Budget: $2,000/month for infrastructure
- Team: 3 developers with Node.js experience
- Timeline: 8 weeks to production

CONSTRAINT ANALYSIS:
Performance constraint → Need caching, load balancing, efficient database
Budget constraint → Must optimize cloud costs, consider serverless
Team constraint → Stick to Node.js ecosystem, avoid complex new tech
Timeline constraint → Use proven patterns, minimize custom development

SOLUTION DESIGN:
Given these constraints, let me design the optimal architecture:

1. Technology choices (considering team skills and timeline)
2. Architecture patterns (considering performance and budget)
3. Implementation strategy (considering timeline and resources)
4. Monitoring and optimization plan (considering performance goals)

Create a messaging system that can evolve from MVP to enterprise scale:

EVOLUTION PLANNING:
Phase 1 (MVP - Month 1):
- Basic messaging between users
- Simple WebSocket connections
- In-memory message storage
- Support 100 concurrent users

Phase 2 (Growth - Month 3):
- Message persistence with database
- User presence and status
- File sharing capabilities  
- Support 1,000 concurrent users

Phase 3 (Scale - Month 6):
- Horizontal scaling with load balancers
- Message queuing system
- Advanced features (groups, notifications)
- Support 10,000 concurrent users

Phase 4 (Enterprise - Month 12):
- Multi-region deployment
- Advanced security features
- Analytics and reporting
- Support 100,000+ concurrent users

ARCHITECTURE DECISIONS:
Choose technologies and patterns that support this evolution:

Investigate this production bug systematically:

BUG REPORT: "Users occasionally see other users' data in their dashboard"

SYSTEMATIC INVESTIGATION:
Step 1: Understand the symptoms
- What exactly is being shown incorrectly?
- How frequently does this occur?
- Which users are affected?
- What actions trigger the issue?

Step 2: Form hypotheses
- Cache key collisions between users
- Session management issues
- Database query filtering problems
- Race conditions in data loading

Step 3: Design tests for each hypothesis
- Cache hypothesis → Check cache key generation logic
- Session hypothesis → Review session handling code
- Query hypothesis → Analyze database query filters
- Race condition hypothesis → Load testing with concurrent users

Step 4: Execute investigation plan
[Implement systematic testing approach]

Choose between monolithic vs microservices architecture:

SYSTEMATIC DECISION PROCESS:
Step 1: Analyze current requirements
- Team size and expertise
- Performance requirements
- Scalability needs
- Deployment constraints

Step 2: Evaluate each option
Monolith pros/cons:
+ Simpler development and deployment
+ Better performance for simple use cases
- Harder to scale individual components
- Technology lock-in

Microservices pros/cons:
+ Independent scaling and deployment
+ Technology diversity
- Increased complexity
- Network latency and reliability issues

Step 3: Apply decision criteria
[Weight factors based on project needs]

Step 4: Make recommendation with reasoning
[Choose optimal architecture with justification]