When integrating AI models into applications, provide consistent configuration methods that support different access patterns (direct API, chat interfaces, or intermediary services like LiteLLM). Key practices: 1. Support environment variables for flexible configuration (e.g., `LITELLM_BASE_URL`, `OPENAI_API_KEY`) 2. Handle both local and remote model access scenarios 3. Account for behavioral differences between chat and API modes of the same model 4. Use clear prefixing for different interaction modes Example: ```python def create_model(model_name, **kwargs): # Support prefix-based mode selection COPY_PASTE_PREFIX = "cp:" copy_paste_mode = model_name.startswith(COPY_PASTE_PREFIX) if copy_paste_mode: model_name = model_name.removeprefix(COPY_PASTE_PREFIX) # Support environment variables for configuration base_url = os.environ.get("MODEL_API_BASE_URL") api_key = os.environ.get("MODEL_API_KEY") # Create appropriate client based on configuration if base_url: client = RemoteModelClient(base_url, api_key, model_name) else: client = LocalModelClient(model_name) return client ``` This standardized approach improves maintainability when supporting multiple AI backends and simplifies switching between different deployment configurations.