James Western

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Classification

Question Answering

RLHF

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I am a highly motivated electronics engineering graduate with hands-on experience in data annotation, AI model quality reviewing, and prompt engineering. My work has involved evaluating AI-generated responses across multiple axes such as instruction following, helpfulness, truthfulness, relevance, safety, and tone, with a particular focus on STEM topics and complex electronics questions. Through these projects, I have developed a deep understanding of the limitations and potential of large language models, including identifying hallucinations and ensuring safe, accurate outputs. My technical background includes programming in Python, JavaScript, SQL, and C++, as well as experience with version control and embedded systems. I am passionate about leveraging my analytical skills and AI knowledge to improve data quality and contribute to the development of reliable AI systems.

Labeling Experience

Data Annotation – AI Model Quality Reviewer

Data Annotation TechTextClassificationQuestion Answering

I participated in a range of projects focused on providing structured feedback to enhance the quality of AI-generated responses. My work entailed reviewing and comparing AI outputs along multiple axes, such as instruction following, helpfulness, truthfulness, relevance, safety, and tone. Projects included fact-checking response claims, creative writing assessments, and providing guidance for LLMs in complex electronics topics. • Conducted comprehensive evaluations of AI model outputs for relevance and factual accuracy. • Applied rubrics and taxonomies to assess and compare AI responses in various domains. • Used STEM knowledge and critical reasoning to verify claims and technical details in AI-generated content. • Gained insight into AI model limitations, including hallucinations and aspects of safety and appropriateness.

I participated in a range of projects focused on providing structured feedback to enhance the quality of AI-generated responses. My work entailed reviewing and comparing AI outputs along multiple axes, such as instruction following, helpfulness, truthfulness, relevance, safety, and tone. Projects included fact-checking response claims, creative writing assessments, and providing guidance for LLMs in complex electronics topics. • Conducted comprehensive evaluations of AI model outputs for relevance and factual accuracy. • Applied rubrics and taxonomies to assess and compare AI responses in various domains. • Used STEM knowledge and critical reasoning to verify claims and technical details in AI-generated content. • Gained insight into AI model limitations, including hallucinations and aspects of safety and appropriateness.

2023 - 2025

Education

Work History