workflow-fine-tuning-gemini

To generate supervised fine-tuning datasets for Gemini models, you can orchestrate multi-step workflows that synthesize question-answer pairs from your project documentation. The process applies iterative QA-driven synthesis and reviewer loops to validate the final training data.

You can automate the QA review process for SFT datasets by running orchestrated answers-questions and revisor loops. This workflow automatically enforces up to five correction iterations per reviewer loop to refine and validate the generated training examples.

QA-driven dataset synthesis requires a project title, a questions file, a logs directory, a documentation directory, and the desired number of output lines. Interaction logs are specifically required to perform risk assessments before generating the final dataset.

If a reviewer loop exceeds the maximum of five correction iterations during dataset generation, the workflow pauses and seeks explicit user authorization before proceeding. This safety mechanism ensures quality control and prevents unbounded loops.

Iterative dataset synthesis handles risk assessment by requiring interaction logs as a mandatory input. The workflow evaluates these logs during the question-answer generation and revision loops to ensure the synthesized training examples meet safety and governance standards.

The final output of an automated fine-tuning dataset generation workflow is a validated dataset file path. This path points to the synthesized training examples ready for downstream supervised fine-tuning of Gemini-family models.