Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship

An LLM cascade can optimize RAG generation by starting with a cheap, local model for tasks like document field extraction. This approach significantly reduces costs by avoiding the default use of expensive flagship models for every task. If the initial model's output fails a validation check, the system automatically escalates the task to a more powerful model. The effectiveness of this method relies on strong validation loops and enriching the prompt with relevant business vocabulary, which often provides a greater performance boost than simply using a larger model.