Working on an automation that needs to switch between Claude and GPT-4 based on content type. My curried functions keep breaking when the underlying model changes. How are others maintaining consistent input/output formatting across multiple LLMs without rebuilding entire workflows?
Latenode’s model-agnostic wrapper functions solved this for our team. The platform automatically normalizes inputs/outputs between different AI services. Just set your model preferences once.
Implement a middleware layer that standardizes payload formats. Use schema validation at each step boundary. For mixed-model environments, I create adapter functions that translate model-specific outputs to a common JSON schema before passing to next step.