It's a crazy market right now. Since LLMs provide 0 analytics insight, all of the available optimization tools are pretty trying to guess or reverse-engineer prompts and their answers. Since it's still pretty much a black box, I think most of the effort is being spent in experimenting.
The one concrete thing I've noticed are some companies changing their SEO blog strategies. Where previously they tried to position themselves as thought-leaders, I've seen an increase if blog posts where they add transparency to what they offer (very clear product descriptions, pricing, use cases, etc.). I believe the general idea is that this type of summarized content is more likely to be picked up by LLMs.
Disclaimer: I've built one of these AI visibility tools (Cartesiano.ai), so I've seen just how much noise & uncertainty is around this space.
This is why most of these AI search visibility tools focus on tracking many possible prompts at once. LLMs give 0 insight into what users are actually asking, so the only thing you can do is put yourself in the user’s shoes and try to guess what they might prompt.
Disclaimer: I've built a tool in this space (Cartesiano.ai), and this view mostly comes from seeing how noisy product mentions are in practice. Even for market-leading brands, a single prompt can produce different recommendations day to day, which makes me suspect LLMs are also introducing some amount of entropy into product recommendations (?)
I don’t think there’s a clean solution yet but I’m not convinced brute force prompt enumeration scales either, given how much randomness is baked in. I guess that’s why I’ve started thinking about this less as prompt tracking and more as signal aggregation over time. Looking at repeat fetches, recurring mentions, and which pages/models seem to converge on the same sources. It doesn’t tell you what the user asked, but it can hint at whether your product is becoming a defensible reference versus a lucky mention.
From someone who's built a tool in this space, curious if you’ve seen any patterns that cut through the noise? Or if entropy is just something we have to design around.
Disclaimer: I've built a tool in this space as well (llmsignal.app)
We just started a blog a few months ago at our company on a variety of topics such as mobile & BE development, as well as design [1]. Hopefully it's of use to the community :)
Picnic is the world’s fastest growing online supermarket with a simple mission: to make grocery shopping quick, easy and affordable for everyone.
Our unique tech-driven approach enables us to work towards a greener and more sustainable future, with our fleet of 100% electric vehicles delivering fresh products from our warehouse to customers daily.
Yeah, it requires Android 4.0 or newer to run. It was a decision we made at the beginning since we don't own Android devices running those versions and didn't want to spend all of our time making the app compatible with all android versions (or at least starting at froyo).
The one concrete thing I've noticed are some companies changing their SEO blog strategies. Where previously they tried to position themselves as thought-leaders, I've seen an increase if blog posts where they add transparency to what they offer (very clear product descriptions, pricing, use cases, etc.). I believe the general idea is that this type of summarized content is more likely to be picked up by LLMs.
Disclaimer: I've built one of these AI visibility tools (Cartesiano.ai), so I've seen just how much noise & uncertainty is around this space.
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