Bridging 20+ years of systematic optimization and quality control expertise with cutting-edge AI prompt engineering. Specializing in agricultural technology applications and process automation.

I design, test, and optimize prompts for large language models to solve real-world problems. My unique background in systematic quality control and agricultural operations gives me a distinct edge in creating prompts that are both technically sound and domain-specific.

Core Focus Areas:

• 🧠 Prompt engineering for Claude, GPT-4, and other LLMs
• 🧪 Automated testing frameworks for LLM outputs
• 🌱 Agricultural and biotech AI applications
• 📊 Data-driven prompt optimization
• 📝 Technical documentation and process design

Comprehensive collection of prompt templates, methodologies, and case studies demonstrating:

• Chain-of-thought prompting techniques
• Few-shot learning optimization
• Systematic testing and iteration processes
• Before/after case studies with measurable improvements

Specialized prompts for agriculture, horticulture, and biotechnology:

• Cultivation management and optimization
• Pest and disease identification systems
• Breeding program documentation
• Regulatory compliance and quality control
• This is my unique differentiator - combining 20+ years of agricultural expertise with AI

Python-based framework for automated prompt testing and evaluation:

• Batch testing across multiple models
• Performance metrics and comparison tools
• Quality assurance workflows
• Reproducible testing methodologies

Tools for systematic prompt refinement:

• Performance tracking and analytics
• A/B testing frameworks
• Iteration documentation
• Data visualization dashboards

Coming from a background in quality control and process optimization, I apply the same rigorous methodologies to prompt engineering:

1. Systematic Testing - Every prompt is tested across multiple scenarios and edge cases
2. Iterative Refinement - Document what works, what doesn't, and why
3. Quality Metrics - Measure performance objectively with clear success criteria
4. Comprehensive Documentation - Every prompt includes context, methodology, and results
5. Domain Expertise - Leverage real-world experience to create prompts that solve actual problems

AI & Prompt Engineering

Prompt Engineering • Natural Language Processing • LLM Optimization
Chain-of-Thought Prompting • Few-Shot Learning • RAG Systems
Model Evaluation • Quality Assurance • A/B Testing

Programming & Development

Python • JavaScript • SQL • Ruby • Git/GitHub
API Integration • Agile Development • Software Testing

Tools & Platforms

OpenAI API • Anthropic Claude • Google Gemini
Jupyter Notebooks • VS Code • Postman
Excel/Data Analysis • CRM Systems • Project Management

Domain Expertise

Agriculture • Horticulture • Biotechnology
Quality Control • Process Optimization • Regulatory Compliance
Team Leadership • Technical Documentation

Professional Transition: After 20+ years in agricultural operations management, I'm applying my expertise in systematic optimization, quality control, and process improvement to AI prompt engineering. My background provides a unique perspective that combines technical skills with deep domain knowledge.

Key Transferable Skills:

• ✅ 20+ years of iterative testing and optimization
• ✅ Extensive quality assurance and compliance experience
• ✅ Technical documentation and process design
• ✅ Team leadership and training (11-20 member teams)
• ✅ Cross-functional collaboration and problem-solving

Education:

• Associate of Applied Science (A.A.S.) - Web Development & Computer Science
• Spokane Community College, 2008-2010

Most prompt engineers come from pure software backgrounds. I bring:

1. Domain Expertise - Deep knowledge in agriculture/biotech that translates to specialized AI applications
2. Quality Control Mindset - 20+ years of rigorous testing and validation experience
3. Systematic Approach - Proven track record of optimizing complex processes
4. Documentation Excellence - Experience creating clear, comprehensive technical documentation
5. Real-World Problem Solving - Focus on practical applications, not just theoretical concepts

I'm actively seeking opportunities in AI prompt engineering, LLM optimization, and agricultural technology applications.

• Email: ggarrod76@gmail.com
• LinkedIn: linkedin.com/in/gabriel-garrod
• Location: Creston, WA (Open to remote work)
• GitHub: You're already here! Feel free to explore my repositories

• 🌱 Expanding AgTech AI prompt library with real-world use cases
• 🧪 Building comprehensive testing frameworks for prompt evaluation
• 📚 Exploring advanced techniques: Tree-of-Thought, ReAct, and multi-agent systems
• 🤝 Contributing to open-source AI projects
• ✍️ Writing about the intersection of agriculture and AI

Interested in:

• AI applications in agriculture and biotechnology
• Prompt engineering best practices and methodologies
• Quality assurance frameworks for LLMs
• Technical documentation and training materials
• Open-source AI tools and utilities

Completed:

• Prompt Engineering Fundamentals (Self-Directed Learning)
• Python for Data Analysis
• Machine Learning Basics

In Progress:

• Advanced Prompt Engineering Techniques
• LLM Fine-Tuning and Optimization
• RAG System Design

Last Updated: October 2025