# READY-TO-USE TEMPLATES

Production-ready templates and guides for building LLM applications with design patterns. These templates are optimized for Claude Code integration and rapid enterprise development.

## 🚀 Essential Decision Guide

### [🎯 LLM Pattern Decision Guide](claudecode_templates/llm_pattern_decision_guide)

**The most important resource for LLM system development.**

**What it provides:**

* **Instant Pattern Selection**: Decision matrix based on your specific requirements
* **Production-Ready Templates**: Code from real enterprise systems (LiteLLM, FastMCP, ByteDance)
* **ROI Insights**: Expected cost savings and implementation time
* **Anti-Pattern Warnings**: Common mistakes and proven solutions

**Quick Examples:**

* Need multi-provider AI? → `Adapter + Strategy` (60-80% cost reduction)
* Building AI tools? → `Decorator + Registry` (80-90% dev time savings)
* Enterprise gateway? → `Proxy + Observer` (Full production control)

> **💡 Perfect for**: Claude Code pattern selection, rapid prototyping, enterprise deployments

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## 📦 Project Templates

Ready-to-use project templates for building specific types of AI applications with proven architectural foundations.

### [AI Agent System Template](claudecode_templates/ai_agent_system_template)

**Design Patterns**: Template Method, Factory, Chain of Responsibility, Strategy, Command\
**Use Case**: Multi-agent workflows, intelligent task routing, LangChain-style systems\
**Based On**: ByteDance Trae-Agent architecture

**Features:**

* Agent workflow orchestration
* Multi-step reasoning pipelines
* Tool composition and chaining
* Configurable agent behaviors

### [Multi-LLM Provider Template](claudecode_templates/multi_llm_provider_template)

**Design Patterns**: Abstract Factory, Strategy, Facade, Observer, Command\
**Use Case**: Cost-optimized LLM integration, provider failover, vendor independence\
**Based On**: LiteLLM enterprise architecture

**Features:**

* Provider abstraction layer
* Intelligent routing strategies
* Cost tracking and optimization
* Enterprise monitoring and controls

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## 🔧 Usage Instructions

### For Claude Code Integration

1. **Start with the Decision Guide**: Use `llm_pattern_decision_guide.md` to identify the right patterns for your specific needs
2. **Select Implementation Template**: Choose from the quick implementation templates in the decision guide
3. **Customize for Your Use Case**: Adapt the production-tested code to your requirements
4. **Add Enterprise Features**: Use the advanced pattern combinations for production deployment

### For Manual Development

1. **Read the Decision Guide**: Understand pattern selection criteria and ROI expectations
2. **Choose Project Template**: Use specific templates for common AI application types
3. **Follow Best Practices**: Implement anti-pattern warnings and optimization guidelines
4. **Scale to Production**: Apply enterprise pattern combinations and monitoring

## 🎯 Template Selection Guide

| Your Need                      | Recommended Starting Point               | Implementation Time  |
| ------------------------------ | ---------------------------------------- | -------------------- |
| **Quick pattern selection**    | Decision Guide → Quick Templates         | 30 minutes - 2 hours |
| **Multi-provider integration** | Decision Guide → Multi-Provider template | 2-4 hours            |
| **Agent workflow system**      | Decision Guide → AI Agent template       | 4-8 hours            |
| **Enterprise AI gateway**      | Decision Guide → Enterprise templates    | 1-2 days             |
| **Custom AI application**      | Decision Guide → Pattern combinations    | Variable             |

## 📚 Integration with Project Documentation

* **Pattern Theory**: See [Design Patterns Overview](../../readme#design-patterns-overview) for detailed pattern documentation
* **Enterprise Examples**: See [Enterprise Cases Analysis](../../readme#enterprise-cases-analysis) for real-world implementations
* **Interactive Learning**: See [Workshop Projects](../../readme#workshop-projects) for hands-on pattern exploration

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**Last Updated**: 2025-08-15\
**Confidence Level**: Production-tested with proven enterprise ROI
