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LLM Observability: the Why, What and How

Expert panel led by Jake Flomenberg, Partner at Wing Ventures

LLM Observability 2023-12-06 - Hubspot Hero Revision

Why is LLM Observability important? And how does it fit in the LLMOps stack?

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In order for LLM apps to go beyond experimentation to valuable apps in production, developers need confidence that their app is successful and will continue to be effective in live use. Specific challenges include ensuring that your app doesn’t wander off into the wilderness of hallucinations, irrelevance, overconfidence, toxicity, bias, and more. 

Join Jake Flomenberg, partner at Wing Ventures, as he leads a panel of experts discussing how they’ve avoided the pitfalls of LLM apps and what they’re doing about a critical part of the LLM stack: LLM Observability. In this webinar, you will learn:

  • What could possibly go wrong? Why simply hoping for the best is not your friend. We’ll cover the variety of ways that apps run into trouble.
  • Building for resilience - lessons learned: building and deploying applications powered with LLMs, such as OpenAI’s GPT series, and vector databases, such as Pinecone 
  • LLM Observability: what it is, and how evaluation, debugging, and monitoring are your friends

LLM Observability: the Why, What, and How
Live webinar on Dec. 6 at 10am PT / 1PM ET / 6PM GMT

Can't make the live webinar? Watch it later! Register now and we’ll send you the recording after the event.

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Meet the Panelists


Jake Flomenberg
Partner, Wing Ventures


Bratin Saha
Vice President and General Manager, AI and Machine Learning, Amazon

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Adrien Treuille
Head of Streamlit


Anupam Datta
President, Chief Scientist,
and Co-founder, TruEra

Register for Webinar

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About TruEra

TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.

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