Ethan Park | Senior Prompt Engineer at Anthropic

Summary

Senior Prompt EngineerAnthropic

San Francisco, CAAug 2021Present

Led prompt optimization initiatives, improving Claude model response quality by 30% for critical user-facing applications, enhancing user satisfaction.
Developed and implemented A/B testing frameworks for prompt variations, increasing user engagement metrics by 15% across key product features.

Prompt EngineerGoogle AI

Mountain View, CAJul 2018Jul 2021

Engineered prompts for Google's internal LLM, resulting in a 20% improvement in code generation accuracy for developer tools, accelerating internal projects.
Conducted extensive experimentation with few-shot and zero-shot prompting techniques, boosting model understanding for specific tasks by 18%.

Developed an open-source framework for standardized evaluation of prompt engineering techniques across various LLMs and tasks, adopted by 150+ researchers.
Implemented metrics for quantifying prompt robustness and efficiency, contributing to a 20% more accurate comparison of prompting strategies.
Integrated with popular LLM APIs (OpenAI, Hugging Face), facilitating easy benchmarking and community contributions.

LLM Assistant for Developers

Mar 2022Nov 2022

Created a personal LLM-powered coding assistant using fine-tuned models, increasing my personal coding efficiency by 25%.
Designed custom prompt chains for debugging, code generation, and documentation, resulting in 15% fewer errors in development.
Utilized RAG with local documentation to provide highly relevant and accurate responses, demonstrating advanced prompt retrieval techniques.

Built a Python toolkit for identifying and mitigating prompt injection vulnerabilities in LLM applications, reducing successful attacks by 80% in simulations.
Implemented various defense strategies including input sanitization, perplexity scoring, and adversarial prompt detection.
Published initial findings and methodology on GitHub, attracting 50+ stars and forks, fostering community discussion on LLM security.

Stanford UniversityMaster of Science in Computer Science

Stanford, CASep 2016Jun 2018

Specialized in Artificial Intelligence and Natural Language Processing, focusing on generative models.
Published research on advanced prompt-tuning techniques for domain-specific LLM applications.
Graduated with a GPA of 3.9/4.0, demonstrating strong academic achievement.

University of California, BerkeleyBachelor of Science in Computer Science

Berkeley, CASep 2012May 2016

Achieved Dean's List for 7 semesters, maintaining a strong focus on algorithms and data structures.
Awarded 'Outstanding Senior Project' for an innovative NLP-focused conversational AI agent.
Completed program with a cumulative GPA of 3.8/4.0, specializing in AI fundamentals.

Prompt Engineering

Prompt DesignFew-Shot LearningZero-Shot LearningChain-of-ThoughtRetrieval Augmented Generation (RAG)Prompt Injection Defense

Programming & Tools

PythonPyTorchTensorFlowHugging FaceJupyter NotebooksGit

AI/ML Concepts

Large Language Models (LLMs)Natural Language Processing (NLP)Generative AIMachine LearningDeep LearningModel Evaluation

Cloud Platforms

AWS (SageMaker)Google Cloud (Vertex AI)Azure AI ServicesDockerKubernetes

Data Analysis

SQLPandasNumPyData VisualizationA/B TestingExperiment Design