---
name: tai-ch136-fine-tuned-models-and-regression-risk
description: 'Apply chapter 136 of Testing AI, Fine-Tuned Models and Regression Risk, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to fine-tuned models and regression risk.'
---

# Fine-Tuned Models and Regression Risk

Skill name: `tai-ch136-fine-tuned-models-and-regression-risk`

Based on **Testing AI: Engineering Confidence in Non-Deterministic Systems** by **Jason Arbon**.

## Purpose

A fine-tune can improve one behavior while quietly damaging another. Validate the whole model,
not only the task you tuned for.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

Fine-tuning adapts a model to a domain, product voice, output format, tool workflow, label
taxonomy, or specialized task. It can be useful when prompting alone is not enough, when a model
needs to follow a stable style or schema, or when a smaller model needs to perform a narrow task
cheaply.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Deeper Guidance

In production work, treat a fine-tune as a model change with broad behavioral blast radius. Keep
a locked holdout set that the tuning process never sees. Compare base and fine-tuned models with
paired cases when possible. Report target-task improvement, regression slices, severe failures,
confidence intervals, and category-level churn.