Ensure consistent patterns and configurations across all AI model providers to maintain code maintainability and scalability. This includes providing proper default configurations for all providers and minimizing provider-specific conditional logic that can become unwieldy as more AI models are integrated. Key practices: - Always define default model IDs for new AI providers - Use unified configuration patterns rather than provider-specific if/else chains - Apply consistent token limit handling across different providers when possible - Design provider integrations with future model additions in mind Example of inconsistent approach to avoid: ```typescript // Avoid provider-specific logic scattered throughout const maxTokens = apiConfiguration?.apiProvider === "gemini" ? geminiModels[geminiDefaultModelId].maxTokens : anthropicModels["claude-3-7-sonnet-20250219"].maxTokens // Instead, use a unified approach case "sambanova": return getProviderData(sambanovaModels, sambanovaDefaultModelId) // Always provide default ``` This approach reduces technical debt and makes the codebase more maintainable as the number of supported AI providers grows.