Moving from prompt engineering experiments to production-grade AI agents.
Implementing Model Context Protocol to create a standard interface between LLMs and your ecosystem. This allows models to securely read your code, query your SQL databases, and interact with web search or internal APIs in real-time.
Designing the most efficient way to feed data to models. This includes optimized RAG for static knowledge bases and "Just-in-Time" context fetching for dynamic business data, ensuring high accuracy and lower token costs.
Developing automated testing harnesses that treat LLM outputs like software code. Using LLM-as-a-judge frameworks to ensure integrations are safe, reliable, and follow your specific business logic.
Problem: Identifying long-term trends in organizational reputation across
decades of international news was impossible to perform manually with high precision.
Approach: Architected a high-accuracy analysis framework leveraging LLMs
to process 6,500+ articles spanning 1981–2021 from sources like The New York
Times.
Result: Achieved an 86% alignment with human-coded benchmarks, delivering
data-driven insights into the media framing of global economic and humanitarian organizations.