Clinton O.

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

LLM output evaluation and prompt optimization

AI training data annotation and dataset quality assurance

Legal Services & Contract Review

Top Data Types

Text

Document

Image

Top Task Types

Classification

Text Generation

Text Summarization

Fine-tuning

Evaluation/Rating

Transcription

Freelancer Overview

AI Trainer & Prompt Evaluator (Freelance/Contract). Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Airtable, Google Sheets, and Notion. Education includes Bachelor of Science, Alex Ekwueme Federal University, Ndufu-Alike (2025) and Professional Certificate, Tech Sphere Academy (2026). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Trainer & Prompt Evaluator (Freelance/Contract)

Text

Evaluated and scored LLM outputs using structured rubrics for tone, factual accuracy, instruction-following, formatting, helpfulness, coherence, harmlessness, and task alignment. Reviewed 500+ AI-generated text samples and provided a short rationale for each rating to support prompt iteration. Identified recurring failure patterns such as hallucinations, instruction drift, and tone mismatches, then documented annotated examples to improve output quality. • Scored multi-step prompt sequence outputs from GPT-4 and Claude • Maintained structured feedback logs and quality benchmarks • Tracked error categories and rewrite rationales across model versions • Built 20+ prompt templates for lead generation, summarisation, and data extraction tasks

2025 - 2025

AI Data Reviewer & Annotation Specialist (Contract)

Text

Annotated and quality-checked AI training datasets across text classification, sentiment labelling, and instruction-response pair evaluation. Evaluated model-generated outputs against detailed rubrics focused on accuracy, safety, helpfulness, and coherence. Logged rejected samples with structured feedback notes and helped reduce downstream labeling inconsistencies by creating reusable annotation guidelines. • Flagged mislabelled examples and edge cases before training pipelines • Ran red-teaming exercises and logged 60+ flagged samples with failure descriptions • Authored annotation guidelines to cut inter-annotator disagreement by ~40% • Provided structured feedback to support downstream prompt correction

2024 - 2024

Education

Work History