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Archives for October 2024

Will Generative AI Hurt Search & Publishers?

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The short answer seems to be “Yes.”

This isn’t really a new idea at this point. But I wanted to offer my take on it as I don’t think I’ve seen a good overall summary and I wanted to offer one.

The deeper questions seem to be: How will it hurt? How much? And what will Search companies and Publishers do in response? While Generative AI is different from Traditional Search and has different use cases, some aspects are taking market share from traditional search. According to Similarweb back in 2023 “ChatGPT Grew Another 55.8% in March, Overtaking Bing.” And there’s the early 2024 prediction by Gartner of Search volume dropping 25% by 2026. Then AdWeek said that Google’s use of AI alone could hit publishers with a $2B annual ad revenue loss.

This will likely continue and is somewhat ironic. Search will take market share hits because someone else is using content that isn’t theirs to create value. Sound familiar? Seems like the foot is on the other shoe. And yet, while Search might be facing some disruption, their core value propositions remain just fine. Also, they typically are integrated with several products; e.g., Google across web, video, maps, and more. The real question is how they’ll deploy billions to respond, not that anything’s replacing them soon. Still, with hundreds of billions at stake, even a small percentage hit is non-trivial. (Even if Google is growing ad revenue faster than others lately.) We should be more concerned with the smaller search engines and the publishers themselves.

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Prompt Iteration for Consumers – Getting the Best from Generative AI

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There’s a somewhat well-known joke about a photographer that went to a fancy dinner party. The host who was also the chef said to the photographer-guest, “I love your wonderful pictures; you must have a fantastic camera.” After a great dinner, the photographer said to the host, “Dinner was great. You must have a fantastic stove!”

The moral is clear enough. Though our tools can make a difference, going from good to great isn’t usually about the tool, it’s the craftsperson. The question is which kind of user do you want to be? This article isn’t about AI builders using what’s referred to as “Prompt Engineering.” The focus here is for generative AI users who want to improve results without deep technical exploration. Let’s do a little background first to set some context, then bullet point some techniques to try out. (Skip down to the bullet point list if you don’t care for the background.)

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Using AI for Mediocrity at Speed

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I’m a huge fan of LLMs… but this will be some of the subtle dark side. This isn’t an attempt to show AI doing horrid things. No. My concern is more subtle. It’s about insidious degradation of thought; in spite of the awesome power of AI to also help us learn and produce new things. My concern is that AI, eventually feeding upon itself, will regress us to average faster than ever.

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Prompt Engineering: What It Is and Isn’t

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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.

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