In this article, you will learn how to fine-tune an agentic AI system holistically, covering all four critical dials: training data, parameter-efficient fine-tuning, runtime hyperparameters, and preference alignment.
Topics we will cover include:
How to build and validate a well-formatted tool-calling fine-tuning dataset that prevents hallucinated function calls before training ever begins.
How to configure and apply QLoRA for parameter-efficient fine-tuning, and how to tune inference-time hyperparameters such as temperature and retry policy with the same rigor as training hyperparameters.
How to use Direct Preference Optimization (DPO) to teach judgment calls that supervised fine-tuning alone cannot express, and how to evaluate the result with a verdict-driven framework that catches catastrophic forgetting before it ships.
