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Observability for LLMs

Posted Oct 09, 2023 | Views 349
# Observability
# LLMs
# Honeycomb
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Phillip Carter
Principal Product Manager @ Honeycomb

Phillip is on the product team at Honeycomb where he works on a bunch of different developer tooling things. He's an OpenTelemetry maintainer -- chances are if you've read the docs to learn how to use OTel, you've read his words. He's also Honeycomb's (accidental) prompt engineering expert by virtue of building and shipping products that use LLMs. In a past life, he worked on developer tools at Microsoft, helping bring the first cross-platform version of .NET into the world and grow to 5 million active developers. When not doing computer stuff, you'll find Phillip in the mountains riding a snowboard or backpacking in the Cascades.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

Making LLMs reliable is hard. You can't debug or unit test them, not in the traditional sense at least. Instead, you'll need to turn to the practice of Observability, by instrumenting your feature to produce rich telemetry and analyzing behavior from that data. Observability can also act as a key source of data for evaluations.

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