Agents Turn Simple Keyword Search Into Compelling Search Experiences
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The article discusses using AI agents to enhance keyword search experiences, but the discussion reveals a divide between those who value simplicity and determinism in search and those who see potential in AI-driven search improvements.
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- 01Story posted
Sep 23, 2025 at 10:17 AM EDT
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Sep 23, 2025 at 10:37 AM EDT
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Sep 29, 2025 at 2:59 AM EDT
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Not sure what you mean here. Google started with PageRank which is a decent, albeit gamable, ranking algorithm. They’ve never not been leaning heavy on ranking.
Mid 2010s, Google began supporting conversational (non keyword) searches. This is because a good number of queries are difficult to reduce to keywords. But it is an inherently harder problem. And at the same time, the open web enahittified itself, filling up with SEOed blogspam. And a lot of user generated content moved into walled gardens.
This has been deprecated a long time ago (10 years ?). It hurts (ad) sells.
Edit: oops, you used it in another comment on here.
Maybe it is two different products: index search and fuzzy search?
What I understand you to mean is: you ask Google to give you the most-advertised product and hope that it's what's best for you.
Citations needed. Calling recursive bullshit reason does not make it so.
Passing on the cost to you. The fancy search thingy could navigate the AST and catch typos.
I don't thing they do well with search that is built for human engagement, which is a more complex tool to reason about.
> I don't thing they do well with search that is built for human engagement, which is a more complex tool to reason about
I agree! Structured outputs are best.
Adding levels of indirection and secondary reasoning to the search interface makes the results less smooth. This is one of the things humans complain about often when using e.g. Google: "I'm interesting in searching for X, but all these results are Y." Yes, because X and Y are synonyms or close topics and Google is mixing in a popularity signal to deduce that, for example, your search for `tailored swift database` is probably about a corpus of Taylor Swift song lyrics and not about companies that build bespoke Swift APIs on top of data stored in Postgres.
If you're driving the process with an LLM, it's more of a problem for the LLM if it's searching the space and the search engine under it keeps tripping over the difference between "swift means a programming language" and "swift means a successful musician" as it explores the result space. A dumber API that doesn't try to guess and just returns both datasets blended together fits the space-search problem better.
We'd be stupid to ignore the last 15+ years of big tech "democratization"-to-enshittification bait-and-switch.
This article is about how Target can use LLMs to help you find patio furniture. I guess you could imagine the LLM upselling you on a more expensive set?
Adding an LLM or agentic data flow and a tuned prompt to the mix does nothing to change who is in charge, it was never you.
The bigger issue is I’m not sure agents are trained to understand what users find engaging. What makes users click.
It seems plausible and intuitive that simple tools dynamically called by agents would yield better results than complex search pipelines. But do you have any hard data backing this up?
Turn this into a paragraph-sized prompt
Turn this into a n document length formal proposal,
And then split that into paragraph sized token optimized prompts
I want my machine to be determinstic and non-magical. I am so tired of search tools that won't let me actually search for what I want because it clearly thinks I meant something else.
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