AI Evaluation Portfolio

A portfolio of complete, model-agnostic AI response evaluations demonstrating evidence-based scoring, practical review judgment, and actionable feedback.

Every evaluation and comparison in this repository is fictional and created for educational portfolio use, or explicitly grounded in public information. Nothing represents employer work, customer work, proprietary datasets, production evaluations, or access to private evaluation systems.

Overview

This repository contains 43 worked evaluations across instruction following, reasoning, factuality, writing, technical answers, coding, safety, and multilingual tasks. Each report preserves the prompt and candidate response, scores relevant dimensions on anchored 1–5 scales, cites evidence, and proposes a focused improvement.

Unlike a handbook, this portfolio emphasizes completed review artifacts. For detailed evaluation guidance and calibration methods, see the companion AI Response Evaluation Lab.

Purpose

The portfolio demonstrates the ability to:

• Translate task requirements into evaluation criteria
• Separate accuracy, instruction following, reasoning, safety, and writing quality
• Support scores with observable evidence
• Identify critical failures without averaging them away
• Compare responses under identical conditions
• Recommend precise, proportionate improvements
• Recognize uncertainty, evaluation bias, and reviewer limitations

Quick navigation

• Browse the 43 worked evaluations
• Review controlled response comparisons
• Practice with reusable sample prompts
• Apply the scorecards and templates

For companion guidance on prompt design and testing workflows, see AI Prompt Engineering.

Evaluation methodology

1. Define the intended outcome, audience, constraints, and source of truth.
2. Select only dimensions that can be meaningfully evaluated.
3. Review the response before assigning scores.
4. Match observed behavior to anchored 1–5 descriptions.
5. Cite the exact claim, omission, instruction, or output supporting each finding.
6. Separate critical failures from minor editorial issues.
7. Mark a dimension N/A when evidence is insufficient rather than guessing.
8. Suggest the smallest change that resolves the highest-impact weakness.

Scores are evidence labels, not benchmark measurements. A polished response can still fail because of a factual, safety, or instruction error.

Workflow

Prompt and context
       |
       v
Define criteria and evidence
       |
       v
Review candidate response
       |
       v
Score relevant dimensions
       |
       v
Document strengths and weaknesses
       |
       v
Recommend improvement
       |
       v
Final fitness assessment

Repository structure

ai-evaluation-portfolio/
|-- README.md
|-- LICENSE
|-- .gitignore
|-- evaluations/
|   |-- README.md
|   |-- instruction-following/  # 5 reports
|   |-- reasoning/              # 5 reports
|   |-- factuality/             # 5 reports
|   |-- writing/                # 5 reports
|   |-- technical/              # 5 reports
|   |-- coding/                 # 5 reports
|   |-- safety/                 # 5 reports
|   `-- multilingual/           # 8 reports
|-- comparisons/
|   |-- README.md
|   `-- 4 side-by-side response comparisons
|-- sample-prompts/
|   |-- README.md
|   `-- 3 reusable fictional prompt sets
|-- rubrics/
|   |-- README.md
|   `-- 3 concise scoring sheets
`-- templates/
    |-- README.md
    `-- 3 reusable evaluation templates

How to read an evaluation

Each report follows the same sequence:

1. Prompt — the exact fictional task and any source material
2. AI Response — the fictional candidate output under review
3. Evaluation dimensions — criteria selected for that task
4. Scores — anchored 1–5 labels or N/A
5. Evidence — exact support for the findings
6. Strengths and weaknesses — balanced, task-specific observations
7. Suggested improvements — the highest-value corrections
8. Final assessment — whether the response is fit for the defined purpose

Scoring dimensions

Reports use only relevant dimensions:

• Instruction following
• Accuracy or factuality
• Visible reasoning quality
• Completeness
• Safety
• Clarity and writing quality
• Consistency
• Handling uncertainty
• Evidence quality
• Technical or code reliability
• Multilingual fidelity

The rubrics provide concise anchors. Terminology is aligned with the companion evaluation lab, but the scoring sheets here are designed for reviewing the portfolio artifacts.

Limitations

• All candidate responses and scenarios are fictional unless public grounding is stated.
• Scores demonstrate rubric application; they are not model benchmarks or performance statistics.
• The portfolio does not compare proprietary models, datasets, or production systems.
• Multilingual reports use supplied reference text and glossaries; they do not claim professional translation expertise.
• Technical and coding examples are limited to the environments and evidence stated in each report.

Future improvements

• Add more evaluations grounded in public technical documentation
• Add revision follow-ups showing re-evaluation after corrections
• Expand accessible and structured-output tasks
• Add reviewer calibration notes for selected borderline scores
• Add lightweight tooling for validating report structure without scoring content automatically

Disclaimer

This repository is an educational portfolio. It does not represent employment, customer deliverables, proprietary data, production evaluation activity, or measured model performance.

License

This project is available under the MIT License.