You are a senior QA architect and DevOps engineer specializing in test environment design. Create a comprehensive AI Test Environment Setup Guide for the following context:

**PROJECT CONTEXT**
- Application Type: [APPLICATION_TYPE] (e.g., web app, mobile app, microservices, ML pipeline, embedded system)
- Primary Tech Stack: [TECH_STACK] (e.g., React/Node.js/PostgreSQL, Python/Django/MySQL, Java/Spring/MongoDB)
- Team Size: [TEAM_SIZE] (e.g., 5 developers, 50+ engineers, distributed teams)
- Testing Scope: [TESTING_SCOPE] (e.g., unit/integration/E2E, performance, security, chaos engineering)
- Deployment Target: [DEPLOYMENT_TARGET] (e.g., AWS EKS, Azure AKS, on-premise Kubernetes, serverless)
- Compliance Requirements: [COMPLIANCE] (e.g., SOC2, HIPAA, GDPR, PCI-DSS, none)
- Budget Constraints: [BUDGET] (e.g., cost-optimized, enterprise-grade, startup-friendly)

**REQUIRED OUTPUT STRUCTURE**

1. **ENVIRONMENT ARCHITECTURE**
   - Diagram description (text-based) showing: dev, staging, pre-prod, and production-like environments
   - Network topology with VPC/VNet design, subnets, and security groups
   - Service mesh or API gateway configuration for test isolation

2. **INFRASTRUCTURE-AS-CODE**
   - Terraform/CloudFormation/Pulumi templates with key resource definitions
   - Kubernetes manifests for test namespaces with resource quotas
   - Environment-specific Helm values files

3. **TEST DATA MANAGEMENT**
   - Data generation strategies (synthetic, anonymized production, fixture-based)
   - Database seeding and migration scripts for test environments
   - Data privacy controls and PII handling procedures

4. **CI/CD INTEGRATION**
   - Pipeline configuration for automated environment provisioning
   - GitOps workflow (ArgoCD/Flux) for test environment updates
   - Environment promotion gates and quality checks

5. **AI-SPECIFIC TESTING CONSIDERATIONS**
   - Model serving infrastructure (if applicable): Triton, TorchServe, TF Serving
   - A/B test infrastructure for model variants
   - Data drift detection and model performance monitoring setup
   - GPU resource management and queueing for ML workloads

6. **MONITORING & OBSERVABILITY**
   - Test environment health dashboards (Grafana/Prometheus)
   - Log aggregation and trace collection (ELK/Loki, Jaeger/Tempo)
   - Alerting rules for environment degradation

7. **SECURITY & ACCESS CONTROL**
   - Identity and access management for test resources
   - Secret management (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault)
   - Network policies and service-to-service authentication

8. **COST OPTIMIZATION**
   - Auto-scaling policies and scheduled shutdown for non-production hours
   - Spot instance/preemptible VM strategies
   - Resource right-sizing recommendations

9. **DISASTER RECOVERY & RESET**
   - Environment reset procedures and frequency
   - Backup/restore strategies for persistent test data
   - Blue-green or canary deployment patterns for test infra changes

10. **RUNBOOK & TROUBLESHOOTING**
    - Common failure modes and resolution steps
    - Environment debugging commands and tools
    - Escalation procedures for critical test environment issues

**OUTPUT FORMATTING RULES**
- Provide executable code snippets where applicable, wrapped in appropriate markdown code blocks
- Include [PLACEHOLDER] markers for values users must customize
- Add severity ratings (CRITICAL/HIGH/MEDIUM/LOW) to each recommendation
- Create a priority-ordered implementation roadmap with time estimates
- Append a 'Quick Start' section for immediate environment bootstrapping