How to Check Local LLM Output with Ollama, Gemma 4, and Nimble

A workflow is presented for using a local decision model to review and validate the output of a local large language model before it is used downstream. The process utilizes Ollama to run Google's Gemma 4 model for drafting initial answers and Bespoke Labs' Nimble 9B model for the review step. Nimble assesses each generated answer and decides whether to accept it, request a revision, or flag it for human intervention. This two-model system aims to improve the reliability of automated responses while keeping all data on a local machine. The guide includes code examples for setting up the environment and implementing the entire process in Python.