ChatGPT Rebuilt Its Search Tool, I Read The New Language It Speaks via @sejournal, @suganthan
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Suganthan Mohanadasan 2 minutes ago ⋅ 17 min read Suganthan Mohanadasan Co-founder at Snippet Digital Bio Follow On August 16, when ChatGPT ran a web search for me, the tool call was JSON. {"system1_search_query":[{"q":"site:intercom.com Fin AI Agent pricing 2026"}]} On August 20, I asked the same question on the same account. The tool call now looks like this. fast|Intercom Fin AI agent pricing 2026 live chat support|30|intercom.com fast|Gorgias AI Agent pricing 2026 customer support|30|gorgias.com fast|Zendesk AI agents pricing 2026|30|zendesk.com fast|best AI live chat support Intercom Gorgias Zendesk Ada Tidio Crisp reddit|365|reddit.com length|long Somewhere in those four days, OpenAI replaced the JSON with a compact query language of its own. And search_queries, the metadata field that mirrored the fan-out, the field my last article told you to read and the field FanoutFox parses, is gone from the payload entirely. This is Part 4 of the series that started with how ChatGPT picks sources and most recently found the shortlist it writes before it searches. This time I ran the new format across eight questions, spanning commercial, physical goods, local, news and finance, and mapped what the language can say. Everything here is read off my own browser traffic with a single ChatGPT Plus account from a single location. A tool call is now a block of lines, one search per line, fields separated by pipes. The pattern that repeats is a call type, the query, a number, and sometimes a domain. fast|Zendesk AI agents pricing 2026|30|zendesk.com fast is the call type. The query reads like the old fan-out queries, brand names the model wrote in itself, a year, an intent word like pricing. The number and the domain are new, and they’re the interesting part, because they’re parameters the old format had nowhere to put. Each block closes with a directive line, length|long, length|medium or length|short. This one is the survivor of the old format. The JSON at the top of this article had "response_length":"long" in the same position, so it’s been renamed rather than added. It reads as an instruction for how much text to bring back per result, and the values track the job. long fired on the deep product comparison, medium on most lookups, and short on a final call that was verifying two venue names. If that reading is right, most searches pull a bounded excerpt of your page rather than the page, one more reason the sentence that answers the question needs to sit where an excerpt starts. The fields after the query are optional. Some lines are only a query, some add the number, and the domain appears when a site is being targeted. Ask the questions your buyers ask and check the brands written by ChatGPT. The brand names in those queries are the competitive set ChatGPT already holds for your category, and the domains in the slots are the sites it intends to visit. Make sure your site will survive the probing (I have written about this before and made a few tools, so check them out). Here’s the same field across five lines from the questions I asked on August 20. A stock price gets two. Football results get seven. Commercial product research gets 30. Earnings guidance gets 90. I think this number is a recency window in days, matched to how fast the answer goes stale. I haven’t seen any spec for this, so this is my assumption. On the commercial questions I ran, every brand probe had a 30-day window. If the days assumption is right, a pricing or comparison page that hasn’t changed in a month is competing from outside the default window on exactly the queries where brands get compared. Update the pages that answer buying questions inside that cycle, with real changes and visible dates. Same tactic goes to your key pages and content. Check your prompts/keywords. The call types I’ve captured so far, with the real lines. fast is the web search. It’s the successor to the whole old fan-out. product is a catalogue lookup. It appeared on robot vacuums and mattresses, and on none of the software questions I ran. product|Roborock Saros 10R robot vacuum;Dreame X50 Ultra robot vacuum;Eufy E28 robot vacuum Product names, semicolon-separated, with nothing else on the line. It lines up with the product cards and merchant offers I found in the commerce layer back in June. For physical goods, ChatGPT looks products up in a catalogue as well as searching pages about them, and presence in that catalogue is its own game, separate from anything your blog does. If you sell physical products, check whether yours come back with cards, offers and merchant names when ChatGPT recommends them. Cards mean the catalogue knows you. A text mention with no card means the content game is the only one you’re in, and no amount of page work changes what the product call returns. business is a places lookup, and it takes a location. On a coffee question, it fired three times. Here are the first two. business|Dubai, UAE|specialty coffee Dubai Marina;best coffee Dubai Marina business|Dubai, UAE||Blends Specialty Coffee;Roast Speciality Coffee, Marina;OLAB Coffee Shop JBR The first call sends search phrases. The second sends actual business names it got back from the first, and then verifies them one by one with quoted fast searches. That’s the local version of the shortlist mechanics from the shortlist article, running on venues instead of brands. If you run a local business, the unit of optimisation is your entity in the places data. The lookup returns the name, the reviews, and the details, and the web searches only verify them. Ask ChatGPT for your own category in your own area and watch the second business call. If your venue’s name appears in it, the lookup knows you and the follow-up searches are checking your details, so make sure what’s on your site matches what the listings say. If your name never enters that call, your problem is the places data, and your website can’t fix it.
Source: Search Engine Journal
This article has been carefully curated and reformatted for educational and informational purposes. Full credit goes to the original publisher.
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