---
name: tai-ch056-appendix-using-ollama-for-private-ai-testing
description: 'Apply chapter 56 of Testing AI, Appendix: Using Ollama for Private AI Testing, 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 appendix: using ollama for private ai testing.'
---

# Appendix: Using Ollama for Private AI Testing

Skill name: `tai-ch056-appendix-using-ollama-for-private-ai-testing`

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

## Purpose

When test data is internal, proprietary, regulated, or HIPAA-like, local model workflows can let
testers evaluate behavior without casually sending sensitive examples to cloud APIs.

## 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

Ollama is useful for testers because it makes local LLM testing approachable. You can run
supported open models on your own machine or controlled infrastructure, call them through a
local API, and use them in eval workflows without every prompt leaving the environment. For
example, a healthcare-adjacent team may need to test summarization quality on de-identified
clinical-style notes, internal policy text, or synthetic protected-health-information cases. A
local Ollama setup can support early evaluation while the team works through privacy,
compliance, and approval requirements.

## 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, Ollama-based testing should be treated as private eval infrastructure, not
as a tool tutorial. Use network isolation when needed, disable unnecessary logging, pin model
artifacts, document hardware and quantization, compare local results against stronger reference
models on safe data, and never confuse local execution with legal compliance.