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Chatbot and LLM Analytics with Eric Griffing

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Manage episode 417460809 series 2093893
Content provided by Kane Simms. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kane Simms or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

From the granular details of measuring intent and LLM-based chatbot effectiveness to the strategic implementation of AI across business units, Eric provides an in-depth analysis on the importance of data-driven decisions in the era of AI transformation.


Join us as we discuss how businesses can leverage analytics to optimise customer interactions, the common pitfalls in current AI strategies, and the future of AI in customer service.

In this episode

00:00 Introduction and UNPARSED conference - Find out more at https://unparsedconf.com

4:32 Welcom Eric Griffing

7:25 Best practice for measuring LLM agents

12:10 Differences tracking ML and LLM bots

14:00 Goal completion

19:00 What's missing

22:00 How to set up tracking on chatbots

24:50 Tracking LLM chatbots

30:37 Air Canada's chatbot issues

33:38 LLM guardrails

37:00 Using data to inform your AI strategy

41:00 Effort over satisfaction

44:30 Tracking customer effort in chatbots

48:40 Market demand for AI analytics

55:40 Wrap up


Subscribe to https://vux.world/subscribe for weekly updates on how to implement AI effectively in your organisation.


Learn more from Dashbot:

https://www.dashbot.io/blog/360-bot-framework

https://www.dashbot.io/blog/inside-the-black-box-analyzing-your-llm-bot

https://www.dashbot.io/blog/navigating-the-shift-from-intent-based-to-language-model-chatbots


Unparsed, the world's most loved conversational AI conference returns with more punch, more passion and more impact! Buy your tickets now here



Hosted on Acast. See acast.com/privacy for more information.

  continue reading

311 episodes

Artwork

Chatbot and LLM Analytics with Eric Griffing

VUX World

41 subscribers

published

iconShare
 
Manage episode 417460809 series 2093893
Content provided by Kane Simms. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kane Simms or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

From the granular details of measuring intent and LLM-based chatbot effectiveness to the strategic implementation of AI across business units, Eric provides an in-depth analysis on the importance of data-driven decisions in the era of AI transformation.


Join us as we discuss how businesses can leverage analytics to optimise customer interactions, the common pitfalls in current AI strategies, and the future of AI in customer service.

In this episode

00:00 Introduction and UNPARSED conference - Find out more at https://unparsedconf.com

4:32 Welcom Eric Griffing

7:25 Best practice for measuring LLM agents

12:10 Differences tracking ML and LLM bots

14:00 Goal completion

19:00 What's missing

22:00 How to set up tracking on chatbots

24:50 Tracking LLM chatbots

30:37 Air Canada's chatbot issues

33:38 LLM guardrails

37:00 Using data to inform your AI strategy

41:00 Effort over satisfaction

44:30 Tracking customer effort in chatbots

48:40 Market demand for AI analytics

55:40 Wrap up


Subscribe to https://vux.world/subscribe for weekly updates on how to implement AI effectively in your organisation.


Learn more from Dashbot:

https://www.dashbot.io/blog/360-bot-framework

https://www.dashbot.io/blog/inside-the-black-box-analyzing-your-llm-bot

https://www.dashbot.io/blog/navigating-the-shift-from-intent-based-to-language-model-chatbots


Unparsed, the world's most loved conversational AI conference returns with more punch, more passion and more impact! Buy your tickets now here



Hosted on Acast. See acast.com/privacy for more information.

  continue reading

311 episodes

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