Kristina Dieter is a software engineer and technical educator specializing in applied artificial intelligence. She has spent the past eight years building production ML systems, first in computer vision at an autonomous robotics startup and later in natural language processing at a cloud infrastructure company. Her work has spanned the full stack of modern AI development, from training custom models on proprietary datasets to deploying large language models in customer-facing applications serving millions of requests per day.
In addition to her engineering work, Kristina has taught practical AI courses at the university level and through online platforms. Her approach to teaching is rooted in the belief that the best way to learn a technology is to build something real with it. She avoids abstract theory when a concrete example will do, and she structures her curriculum so that students experience both success and failure in a controlled environment where failure is a learning tool rather than a setback.
Kristina is active in the open source community as a maintainer of several projects in the LLM tooling ecosystem, including contributions to LangChain's agent framework and the Chroma vector database project. She regularly speaks at industry conferences about production AI, pragmatic evaluation strategies, and the gap between academic NLP and real-world deployment.
She lives in Portland, Oregon, where she hikes, reads science fiction, and maintains a running debate with her colleagues about whether prompt engineering will still be relevant in five years. Her current prediction is that the specific techniques will change, but the underlying skill of communicating intent to a language model will become more important, not less.