Summary
Highly analytical and innovative Prompt Engineer with 6 years of experience specializing in optimizing large language models (LLMs) for enhanced performance and user interaction. Proven track record in designing, testing, and refining prompts to achieve desired AI outputs, significantly improving model accuracy and relevance. Adept at leveraging deep understanding of natural language processing and machine learning to drive product innovation and solve complex challenges in AI development.
Experience
Senior Prompt EngineerAnthropic
- 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
- 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%.
Projects
OpenPrompt Benchmark
- 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
- 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.
Prompt Injection Defense Toolkit
- 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.
Education
Stanford UniversityMaster of Science in Computer Science
- 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
- 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.
Skills
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