Schaun Wheeler and Arpit Choudhury talk about Content Management Systems (CMS) and how they are a treasury of information that can be leveraged for hyper-personalization. They explore how generative AI is transforming CMS, the importance of data quality, and the potential of LLMs in enhancing user experiences. The discussion also covers the challenges of implementing AI in recommendation systems, the significance of integrating external data, and the future of personalization in various applications, including food delivery and travel.

KEY POINTS

* CMSs are often seen as repositories of content to present on webpages but they can be structured to use data for purposes beyond webpage display such as personalization, communication, and discovery. LLMs can significantly improve CMS data management.

* There are opportunities in what you bring in and what you take out of the CMS. Information provided by vendors can be better used by turning it into structured data.

* Generative AI can be leveraged to dynamically change the way content is displayed to users each time.

* Cleaning your CMS can allow you to extract additional value from it. LLMs enable on-the-fly data cleaning and enhancement without the massive manual effort previously required. They can also analyze content tone, create detailed sub-categories, and generate descriptive tags. This data can power features like sophisticated filters and AI-driven personalization through agentic learning.

* The alignment problem in AI is when there is a gap between user expectations and AI recommendations. The hardest part of aligning input to output in an LLM is getting the context right.

* In a CMS, an input is anything that resides in the CMS, such as item IDs, item names, and item descriptions. An output is information that is presented to the user.

* LLMs work when choosing between a limited number of options. When there are hundreds of thousands of items, LLMs aren't able to sort through all of the information consistently and coherently.

* LLMs can be used to clean your CMS data and to further personalize the presentation of information. They can extract tone, feel, and other metadata that enhance both recommendations and browsing experiences.

* Lots of information can be pulled in from various sources to address the discovery problem, but it must be presented selectively. Companies that help users find relevant information quickly will retain users better than those who don't.

* A/B testing tells you what works for the largest minority of users, so giving everyone that experience is a bad idea. We can use AI agents to learn recommender preferences and cater to the changing needs of each user, as they are scalable and can give each user the required attention.

CHAPTERS:

* 00:00 Introduction

* 01:22 Developments in CMS technology

* 03:45 Clean your CMS

* 07:34 Leveraging tags for recommendation

* 09:42 The alignment problem

* 14:53 Applications of LLMs

* 19:14 Data extraction capabilities of LLMs

* 25:29 Value propositions in food delivery

* 28:36 The potential of CMS

* 31:34 Integrating external data

* 33:45 Agentic applications in CMS

* 40:13 Inputs and outputs

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