Episode 51: Why We Built an MCP Server and What Broke First

Vanishing Gradients

Hugo Bowne-Anderson에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Hugo Bowne-Anderson 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

4M ago 47:41

MP3•에피소드 홈

What does it take to actually ship LLM-powered features, and what breaks when you connect them to real production data?

In this episode, we hear from Philip Carter — then a Principal PM at Honeycomb and now a Product Management Director at Salesforce. In early 2023, he helped build one of the first LLM-powered SaaS features to ship to real users. More recently, he and his team built a production-ready MCP server.

We cover:
• How to evaluate LLM systems using human-aligned judges
• The spreadsheet-driven process behind shipping Honeycomb’s first LLM feature
• The challenges of tool usage, prompt templates, and flaky model behavior
• Where MCP shows promise, and where it breaks in the real world

If you’re working on LLMs in production, this one’s for you!

LINKS

🎓 Learn more:

Hugo's course: Building LLM Applications for Data Scientists and Software Engineers — next cohort starts July 8: https://maven.com/s/course/d56067f338

📺 Watch the video version on YouTube: YouTube link

61 에피소드

#Tech #Hugo BowneAnderson Its #Data Science #Machine Learning