TetraMesa

GenAI and Search: Differences from a Product & UX Perspective

By

Note: This article is from a Product Manager and Information Architecture perspective. It’s not a consumer guide on how to search better using GenAI.

As Product people, the things we care about most deeply are in the problem spaces. What challenges are we trying to solve? In the fast-changing world of information retrieval, it’s useful to have an understanding of underlying motivations for customer behaviors.

Before we start scrambling to slap an AI prompt input field on top of whatever we’re already selling, we’re going to look at some of the “Why.” Why do people use some of these tools. What is it they really seek? As product managers, we come from diverse backgrounds. Not all have depth in basic information retrieval backgrounds. It’s going to be important to understand some of these concepts as you and your teams will likely be working on projects that will need them. And you may need to consider P&L or similar concerns in these areas.

We’re going to explore use case differences in search vs. some of the newer Large Language Model (LLMs) and Generative AI (GenAI) tools with a longer term goal of how we can do a solid job crafting product that makes use of GenAI experiences. (Including those that go beyond the search use cases.) To do this will take a few steps. The first is making sure we’re thinking about the problem spaces of users and the use cases of traditional search and now generative AI from customer use case perspective. There are many use cases beyond this. Various AI tools can be used for IoT needs, Agent inputs/triggers, Oracle data for blockchain Smart Contracts (arguably these are just agentic triggers as well), and more. (Not to mention multi-modal object types.) These situations offer good cause to evaluate architectures at a deep information architecture level. But for now, we’re going to focus on the day-to-day human interface and we’ll start with basic search. In upcoming articles, we’ll look at design patterns and additional resources for those who want to go deeper.

It’s useful to start with traditional search. Partly as a kind of warm up to get us thinking about how to solve new kinds of problems. Also because there’s overlap with GenAI and we can build on search towards better understanding of how to deploy GenAI.

[Read more…]

Strategic Responses to GenAI from Search

By


I initially planned to write about how Generative AI (GenAI) might impact traditional search in a classic startup vs. incumbent scenario. Over time, as the landscape rapidly evolved, first I shared thoughts on “Search Tools in a GPT World” and then “Traditional Search vs. GPT Business Models.” This all leads to the obvious question… what are traditional search engines doing and what else might they do to respond?

[Read more…]

Traditional Search vs. GPT Business Models

By

What About Business Models?

In the first part of this article series, “Search Tools in a GPT World,” we looked at Search Tools in a product environment where AI GPTs are clearly on a tear. Now we’ll look at how technology and consumer sentiment shifts are impacting economics and business models.

The rise of GPTs introduces significant shifts in business models underpinning search and information retrieval. Search engines operate primarily on ad-driven models, based on traffic, clicks, and ranking. This impacts income to both search engines and the publishers to whom they drive traffic. We’re going to focus on the search engines themselves. In contrast to traditional search, GPTs seem mostly out of the starting gate with pay-per-use, subscription, or freemium models. This may be a reflection of the resource-intensive nature of generating real-time responses. It’s a simpler business model than search, which depends on a complex ad services ecosystem. As well, ads alone might not be sensible from a P&L perspective. Let’s review some of the business models.

[Read more…]

Search Tools in a GPT World

By

I’ve always enjoyed search, both as user and builder. So from a product perspective, I’ve been fascinated by its evolution and the recent fires lit under the traditional tools thanks to the ascendence of AIs. This will be a three part series. First, Search Tools in a GPT world, then business models, and lastly, how traditional search might respond.

So… How might the “traditional” Search industry evolve in the face of AI GPTs? Let’s take a historical tour to consider some customer pain points and values that various tools deal with and how these are morphing. It’s not as simple as GPTs are better search and it might be useful to consider other technology shifts. Did Video Kill the Radio Star? Maybe. But video didn’t kill radio. At least, not completely. Yet. OK, yes, perhaps the shift decimated revenues, but niche use cases survived through both television and even through more recent digital streaming. Even satellite radio was also able to find a place. Will the information retrieval industry experience something similar with what’s been billed as an even more disruptive technology? Or is this truly something radically different if we consider this shift on the level of industrial revolution?

Will the future of Search follow a similar path? Perhaps somewhat, but maybe not quite the death blow some have suggested given there seem to be a lot of niche values for Search. AI driven GPTs, (Generative Pre-trained Transformers), are already changing the search landscape. But their evolution is not as simply obvious as “this is a better search” for at least two related, but separate reasons. First, GPTs can likely excel past traditional search for a wide variety of use cases. But perhaps not all. And second, GPTs can and are used for significantly different use cases than search.

[Read more…]

How Badly Will the GPTs Kick Google’s Teeth In

By


Notice I didn’t ask “if” newer Artificial Intelligence tools like Generative Pre-trained Transformers (GPTs) will impact Google. I asked “How badly.” While Google isn’t going anywhere, these new tools will chip away at its search market share and perhaps overall value. Google and parent company Alphabet develop properties from mobile operating systems to devices like Nest and Fitbit, Waymo autonomous driving, cloud services, and more. Still, advertising from search, YouTube and their network was almost 80% of their revenues in 2023. Google’s dominance remains and its advertising revenue is still its golden goose. However, GenAI tools introduce a serious competitive threat to the core search business. How badly will Google feel the impact?

Here’s a high level summary of the main points, after which I’ll try to defend each.

  1. Basic Search Needs: AI GPTs are increasingly satisfying basic search needs. While not perfect, they meet many use cases where users are seeking answers; not links to maybe answers.
  2. AI Stickiness: GPTs offer capabilities beyond search, encouraging users to stay in that space. Even with some of the challenges with AI accuracy, people may stick with a “good enough” solution.
  3. GPTs are Improving: These tools are rapidly evolving, fueled by intense investment and innovation.
  4. Google Is Somewhat Stuck: Google’s brand is so tied to traditional search that pivoting may alienate users or undermine its core business.
  5. Everyone is Attacking from Multiple Vectors: Yes. OpenAI’s ChatGPT may have been the alarm bell, but there’s a whole lot more coming; both consumer and business.
  6. Business Use Cases beyond advertising: People are paying premium prices.

That’s the tl;dr. Stop here. Or if you want the backup rationale behind these points, continue…

[Read more…]

Prompt Engineering: What It Is and Isn’t

By

Is Prompt Engineering just an overly fancy way of saying, “Here’s a better search query?” Maybe it should just be called Search 2.0? True enough, the output of an AI large language model is more than just a bunch of results, but the query itself is still ‘just’ an instruction of sorts, right?

In some cases, yes, it’s essentially the same as a fancy query. But mostly not. There are obvious differences in the use cases for prompts using AI Large Language Models (LLMs) vs how keywords get used in traditional search engines. And for all their potential faults and risks, LLMs can provide stunning new capabilities across a variety of use cases. At the same time, there seems to be some overblown expectations as to what prompts can do. For example, at least in some places, a misunderstanding that prompt engineering can make models better. While it may be true that prompts and responses can be iteratively honed and fed back into the fine-tuning of models to actually make models better, for the most part, they’re not used this way. I’d like to try to clear this up because I think it’s important we understand how we can use our tools and where they’re limited. Just to be clear, I’m not talking about the handful of folks who really are evaluating prompt output to adjust models. (If you’re one of those folks, you’re ideally operating more at the data science kind of level of prompt engineering.) For our purposes here, I’m talking about the typical consumer or business use that seems to have some people believing prompt input alone changes how the models themselves work.

[Read more…]

Information Architecture: LATCH Is Not Enough

By

from Craiyon.com AI image generator Keywords-information-architecture

Richard Saul Wurman has been called the father of information architecture. And properly so given that he not only coined the term, but also came up with – among many other things – the acronym of LATCH to describe some core information organization possibilities. So it’s with some trepidation I dare to suggest extending his model. And yet, with time has come our collective experience of dealing with more varied forms and volumes of information via digital channels for which the base model seems dated.

TL;DR version: The LATCH model of information organization includes the following aspects: Location, Alphabet, Time, Category and Hierarchy. It is, however, missing at least the following: Ordinal/Numeric, Distance, and Random. As well, the model lacks depth when it comes to faceted metadata and purpose-focused organization schemes. Okay. That’s it. You’re done. Unless you want to really dive in…

[Read more…]

Customer Journey Map Template

By

While working on a new component of a project, I had occasion to build out a Customer Journey Map. Even though I’ve done this several times before, I’d kind of just cobbled together a map using Omnigraffle or LucidChart or some other drawing program. But this time I had to do a few of them and wanted a more common format template. What I found was a ton of examples in image search, but very few usable editable templates. (There were a few behind some paywalls and seemingly sketchy download requirements, but not much else.)

[Read more…]

Finding Web Page Publish Dates When Not Displayed-Part-2

By

Welcome to Part 2 of Finding Web Page Publish Dates when they’re not displayed on a page. Why would you care? Well, you have your reasons. Please see Part 1 of this topic to get a sense of why we’re bothering to look at this stuff.

In any case, continuing then…

  • See if images have a date stamp.
    • Click on an image or right click and open in a new window. See if the URL has a date stamp on it.
    • A more extreme option might be to look at image info to see if there’s EXIF data in the image with a date. This doesn’t necessarily tell you much as the image could have been taken any time. Maybe it’s years old stock photography.

[Read more…]

Finding Web Page Publish Dates When Not Displayed Part 1

By

In an earlier post, I expressed by own annoyance when certain types of sites choose to not display publish dates on their web pages. And how this is especially annoying when it’s an article that presumes to be talking about current statistics or other aspects where knowing the date context is useful. My goal in that post was to convince any publishers who might happen across the article as to the value of including the date. (Here’s the link to: Should You Put Dates on Blog Posts and Articles?)

This blog is mostly geared towards the Product/Business side of things. But today, it’s more for end users, whom of course may be business users with the need or just desire to find publish dates of content.  I’d like to try to offer web users, surfers, researchers, whatever your self-identifying characteristic may be, some techniques to try to find content publish dates when they’re not clearly provided. These methods are not necessarily accurate, precise, or at all reliable. But they may be all you’ve got.

Why does this matter? Well, it might not. The assumption here is that there are certain types of content, (business research for example), where knowing the date is important enough to warrant the effort to look into the publish date, and possibly the last modified date. [Read more…]

Location

We're located in Stamford, CT, "The City that Works." Most of our in person engagement Clients are located in the metro NYC area in either New York City, Westchester or Fairfield Counties, as well as Los Angeles and San Francisco. We do off site work for a variety of Clients as well.

Have a Project?

If you have a project you would like to discuss, just get in touch via our Contact Form.

Connect

As a small consultancy, we spend more time with our Clients' social media than our own. If you would like to keep up with us the rare times we have something important enough to say via social media, feel free to follow our accounts.