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Episode 51: Why We Built an MCP Server and What Broke First
Published 6 months, 2 weeks ago
Description
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
So We Shipped an AI Product: Did it Work? by Philip Carter (https://www.honeycomb.io/blog/we-shipped-ai-product)
Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)
🎓 Learn more:
Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — next cohort starts July 8: https://maven.com/s/course/d56067f338
📺 Watch the video version on YouTube: YouTube link (https://youtu.be/JDMzdaZh9Ig)
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com
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
So We Shipped an AI Product: Did it Work? by Philip Carter (https://www.honeycomb.io/blog/we-shipped-ai-product)
Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)
🎓 Learn more:
Hugo's course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338) — next cohort starts July 8: https://maven.com/s/course/d56067f338
📺 Watch the video version on YouTube: YouTube link (https://youtu.be/JDMzdaZh9Ig)
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hugobowne.substack.com