LLMOps & Conversational Intelligence for AI

GUEST: Alex Kvamme (@KwameKvamme, CEO @GetPathlight) talks about using AI to unlock market insights from discussions with customers and partners.

SHOW: 777

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SHOW NOTES:

• Pathlight (homepage)
• VentureBeat article on Pathlight & AI Agents
• Article on Real World Productivity of LLMs

Topic 1 - Welcome to the show. Alex, Tell us a bit about your background.

Topic 2 - What is the concept of conversational intelligence and how does it apply to most organizations today? What problem is it trying to solve?

Topic 3 - I would think there is a trade off between time and resources to get to a customer issue vs. the value of that insight. How does an organization weigh the opportunity cost? How do you keep the insights generated from being overwhelming

Topic 4 - Let’s move from the concept to practical. Where is the data in most organizations today that will yield results and solve problems? How would you suggest folks get started and what use case are they likely to implement first? Is this data that humans either can’t or won’t get too because it is an enormous amount or maybe too tedious to pay for?

Topic 5 - How does all of this work under the hood? Is this one model or multiple models working in parallel? Is there a framework for the operations and lifecycle managed by an organization?

Topic 6 - Let’s talk about what it takes to get an LLM into production. The the rise of LLM’s and foundational models such as Llama2, there is an interest for organizations to use LLM’s, but going from concept to production still has a high barrier to entry. It’s easy to download a model, it’s much harder to either fine-tune it or set up RAG with a vector database for your specific use case. Would you agree and what are your thoughts?

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