AI Evaluation Harness

Prototype for scoring, comparing, and reviewing AI outputs before they are trusted inside business workflows or production systems.

Problem

AI output is easy to generate and hard to trust.

A useful AI system needs more than prompts. It needs repeatable evaluation, regression checks, human review boundaries, and clear criteria for what makes an answer acceptable.

What It Does

The AI Evaluation Harness is planned as a lightweight framework for reviewing AI outputs against structured criteria such as:

• accuracy;
• completeness;
• usefulness;
• tone and audience fit;
• evidence quality;
• risk level;
• actionability;
• instruction adherence;
• required human review.

Prototype Direction

The harness is intended to support:

1. Prompt comparisons — compare multiple prompts against the same task.
2. Model comparisons — compare outputs across model/provider options.
3. Regression checks — catch quality drops after prompt or system changes.
4. Rubric scoring — evaluate outputs against clear review dimensions.
5. Human review workflows — mark outputs as approved, needs revision, or rejected.
6. Audit notes — preserve why an output was accepted or changed.

Example Use Cases

• Brand-audit report quality checks.
• SEO/content recommendation review.
• Crawler extraction validation.
• Client-facing copy review.
• Internal AI workflow QA.
• Prompt refactor regression tests.
• Safety and risk scoring before automation.

Planned Architecture

Layer	Responsibility
Test cases	Define task, inputs, expected traits, and constraints
Prompt runner	Execute prompts or workflow steps
Output collector	Store model outputs for review
Rubric engine	Score against structured criteria
Human reviewer	Override, approve, or annotate results
Report exporter	Summarize quality, risks, and next steps

Planned Stack

• Python for scoring utilities and batch evaluation.
• TypeScript / Next.js for review UI concepts.
• JSON or YAML fixtures for test cases and rubrics.
• Markdown reports for portable review summaries.
• GitHub workflows or CLI scripts for repeatable checks.

Human Review

This project is not designed to eliminate human judgment. It is designed to make human judgment more consistent, documented, and scalable.

Current Status

Public proof repository. Documentation-first prototype. Implementation will be built incrementally as reusable evaluation patterns are validated across related AI product lanes.

Related Portfolio

• Prototype Portfolio: https://dev.ross-stretch.com/prototype-portfolio
• GitHub Profile: https://github.com/ross-stretch