Steven Mi
Steven is a Software Engineer with 5+ years of experience building and productionizing machine learning systems that solve high-impact business problems. He has worked across domains including user acquisition, customer retention, content generation, supply optimization, and lead generation.
Currently at GetYourGuide, he is part of the Machine Learning and AI Platform team, where he focuses on enabling teams to independently build and operate production-grade LLM systems.
Outside of work, Steven co-organizes the AI in Production meetup in Berlin, a community dedicated to showcasing real-world AI applications across industries. In his free time, he enjoys exploring food and building tools he'd otherwise have to pay for.
Session
Search works great when you know what you're looking for. But what happens when a user types "sunset tours in Barcelona for a couple under 50 euros"? Traditional search pipelines break down.
We built an AI-powered discovery agent that lets users describe what they want in natural language and get relevant, ranked recommendations. In this talk, we'll walk through the production architecture: a multi-stage LangGraph pipeline that chains intent interpretation, parallel retrieval, LLM-based relevance filtering, and natural language synthesis.
We'll cover the practical trade-offs we made along the way, including a dual-LLM tier strategy (fast nano model for high-volume filtering, full model for nuanced interpretation) that keeps latency and cost in check without sacrificing quality.
We'll also share how we built evaluation infrastructure with 800+ deterministic tests and zero LLM-as-judge in CI, using a pass@k approach with pytest to handle output variability while keeping the test suite fast and reliable. You'll walk away with concrete patterns for shipping LLM-powered features to production: model tiering, deterministic evals, and graceful degradation by design.