Model Chaining

Model chaining is the practice of connecting multiple machine-learning models so that the output of one model becomes the input to the next. This allows complex tasks to be broken into simpler steps, lets you reuse specialized models, and supports structures like linear pipelines or branching workflows to build end-to-end systems that a single model couldn’t easily achieve on its own.