Enterprise Best Practices

AI-assisted development in teams requires standardization, security, and cost control. Without clear guidelines, you get inconsistent code, security risks, and uncontrolled costs.

Team Conventions

Unified AI Usage in the Team

Define clear rules for AI usage:

Mandatory rules:

• Every AI-generated code is manually reviewed (no blind accepting)
• Security-critical code (auth, crypto, payments) is always reviewed by a senior
• AI-generated tests must meet all coverage requirements
• No copy-paste of AI output without understanding how it works

Recommendations:

• Prefer AI for boilerplate, tests, and documentation
• Discuss complex architecture decisions with the team, not alone with AI
• Share new AI patterns in team standup
• Regular "AI Skill Sharing" sessions

Onboarding New Developers

1. Tool setup: Install Cursor, connect team account
2. Explain .cursorrules: Walk through project-specific conventions
3. Share prompt library: Proven prompts for common tasks
4. Pair session: Senior + junior + AI — implement a feature together
5. Review culture: How AI reviews are integrated into the PR process

.cursorrules Standardization

Team-Wide .cursorrules

Create a base .cursorrules for all projects:

# Team Standards

## General
- TypeScript strict mode — no 'any' without comment
- All functions have JSDoc comments
- Error handling: Specific error classes, no generic try/catch
- Logging: Structured logs with context (userId, requestId)

## Testing
- Every new function has unit tests
- Coverage minimum: 80% lines, 70% branches
- Test descriptions in German
- AAA pattern: Arrange, Act, Assert

## Security
- Input validation with Zod at every API boundary
- No secrets in code — always environment variables
- SQL: Prepared statements, never string concatenation
- Auth checks in every protected route

## Performance
- No synchronous operations in hot paths
- Database queries: Select only needed fields
- Pagination for lists > 50 items
- Images: Next.js Image component with explicit dimensions

Security Considerations

Risks in AI-Assisted Development

Security Checklist for AI-Generated Code

• No hardcoded credentials or secrets?
• Input validation at all entry points?
• Output encoding against XSS?
• Prepared statements for database queries?
• Appropriate error handling without stack traces in production?
• Dependencies current and without known CVEs?

Cost Management

Keeping AI Costs Under Control

Typical monthly costs per developer:

Cost control:

• Set token budgets per developer/team
• Model routing: Simple tasks → cheaper model, complex tasks → premium model
• Monthly reporting: Who uses how much? Where do spikes occur?
• Measure ROI: Compare development speed before and after AI adoption

Code Quality Guardrails

Automated Quality Assurance

Build guardrails into your CI/CD pipeline:

1. Pre-commit: ESLint + Prettier + type check
2. Pre-push: Unit tests + coverage check
3. CI pipeline: Integration tests + security scan + license check
4. Code review: AI review (automatic) + human review (manual)
5. Post-merge: E2E tests + performance regression check

Track Metrics

• Code quality score: SonarQube or similar — trend over time
• Bug rate: Bugs per feature after AI adoption vs. before
• Review time: How long do code reviews take?
• Developer satisfaction: Monthly survey on AI tool satisfaction

Enterprise takeaway: AI-assisted development only scales with standards. Invest in .cursorrules, team training, and automated guardrails — then AI accelerates your entire engineering organization, not just individual developers.