Aidan Randle-Conde of Hanzo concludes his blog series on AI success metrics with Part III on bringing it all together – emails and conversational data!

Aidan’s post (Part III: Navigating AI Success Metrics – Bringing It All Together, available here) begins by briefly revisiting Part I on precision, recall and rejection in email and document analysis and Part II on the intricate realm of conversational AI.

Of course, the data landscape involves both emails and conversational data, so Part III navigates the complexities of combining emails and conversational data, the practicalities of using Large Language Models (LLMs), and rethinks the definitions of “Document” and “Recall” to fit this mixed data landscape.

As Aidan notes: “There are…differences between typical values for Recall and Precision between emails and conversational data, with emails typically having higher Recall but lower Precision and conversational data typically having lower Recall but higher Precision. When combining different datasets, it’s useful to keep track of the multiple values for Recall or Precision to reflect the different natures of the datasets.”

So, what are other considerations for the different data types? What is the role of LLMs in these ever-increasingly complex data sets? And how do you adapt review strategies for modern data challenges? Find out here, it’s just one click! Bringing it all together involves reading all three parts of the series! 😀

So, what do you think? Is your organization bringing it all together when it comes to emails and conversational data? Please share any comments you might have or if you’d like to know more about a particular topic.

Image created using GPT-4’s Image Creator Powered by DALL-E, using the term “robot sitting at a desk in front of a single computer monitor showing emails on one side of the monitor and Slack messages on the other side”.

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