When implementing AI model integrations, maintain consistent interfaces and proper type definitions across different providers. This ensures compatibility, prevents runtime errors, and simplifies maintenance. Key guidelines: 1. Define explicit interfaces for model-specific features 2. Isolate provider-specific logic in dedicated classes 3. Use proper type definitions for model responses 4. Maintain consistent parameter naming across providers Example of proper implementation: ```typescript // Define shared interface interface ModelProvider { embed(chunks: string[], task: EmbeddingTasks): Promise; chat(messages: ChatMessage[], options: ChatOptions): AsyncGenerator; } // Implement provider-specific class class DeepSeekProvider implements ModelProvider { // Extend base types for provider-specific features interface DeepSeekMessage extends ChatMessage { reasoning_content?: string; } // Implement shared interface with provider-specific logic async embed(chunks: string[], task: EmbeddingTasks): Promise { // Provider-specific implementation } } ``` This approach: - Prevents interface breaking changes - Makes provider-specific features explicit - Ensures type safety across the application - Simplifies adding new model providers