Panpan N.

Key Skills

Software

Clickworker

CrowdSource

CloudFactory

Top Subject Matter

AI/LLM Evaluation and Dataset Construction

Instructional Design & Professional Communication Training

Human Resources: Recruitment, Interviewing & Labor Relations

Top Data Types

Text

Video

Image

Top Task Types

RLHF

Classification

Entity (NER) Classification

Prompt + Response Writing (SFT)

Evaluation/Rating

Data Collection

Freelancer Overview

As an AI Content Specialist and LLM Evaluator, I possess deep expertise in Instruction Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). I have systematically evaluated mainstream Large Language Models (including ChatGPT and Claude) by designing complex prompt matrices to test model boundaries, identify logical hallucinations, and ensure output accuracy. My unique advantage lies in my ability to bridge the gap between technical model requirements and user intent, having successfully diagnosed and optimized thousands of real-world AI interactions within high-net-worth tech communities. Beyond raw data labeling, I excel at transforming unstructured information into high-quality, structured training corpora. During my intensive AI research project, I produced 26 comprehensive model evaluation reports and built a dedicated AI community, demonstrating an exceptional ability to synthesize complex data and evaluate AIGC quality with high precision. Combined with my MBA background from Shanghai University and leadership training at the University of Cambridge, I bring a strategic, analytical, and highly professional approach to AI data training and quality assurance.

Labeling Experience

Lead AI Evaluator & Content Strategist

TextRLHF

- Model Testing & Hallucination Detection: Conducted rigorous stress tests on leading LLMs (ChatGPT, Claude, Gemini) to identify performance boundaries and logical vulnerabilities. Produced 26 deep-dive evaluation reports focusing on model reasoning and integration. - Intent Annotation & Instruction Optimization: Diagnosed and optimized thousands of real-world user prompts within a high-net-worth AI community. Successfully performed intent recognition tasks to bridge the gap between human requirements and model execution. - Structured Dataset Synthesis: Extracted core logic from unstructured AI industry data to create high-quality training corpora. Expertise in applying industry-standard labeling methods to improve model helpfulness and accuracy.

2026 - Present

Education

Work History