> ## Content Index
> Fetch the complete content index at: https://salienceindex.com/llms.txt
> Use this file to discover other available public pages before exploring further.

# ChatGPT has a dozen indexes and five live experiments. Your prompt list should have no edits
- URL: https://salienceindex.com/chatgpt-has-a-dozen-indexes-and-five-live-experiments-your-prompt-list-should-have-no-edits/
- Published: 2026-09-21T07:39:43.000Z
- Updated: 2026-09-21T07:39:43.000Z
- Author: Anurag Panwar

*The engine under your AI visibility number is being rebuilt in public. The only part of the measurement you control is the list of questions you ask it, and most of the advice on writing that list comes from the companies selling the tracking.*

Method · 21 September 2026

On 4 September Tomek Rudzki, who does research for the Berlin tracking company Peec, published what he had found in the stream of data ChatGPT sends to a browser while it writes an answer. For two months, from 21 May to 21 July, a field called result\_source told him where each result had come from. It only ever carried four values. Three were outside scraping providers. The fourth, Labrador, was OpenAI's own index \[\* S1\].

Labrador turns out to be a family. Rudzki lists a general web index and then a set of vertical ones: PDF, YouTube, news in three tiers by age, arXiv, Wikipedia, local, finance, legal, medical, shopping and images \[\* S1\]. In the middle of August an experiment named prefer-index-over-serp-v3 was touching 8 per cent of chats. On 2 September at least five shopping experiments were running at the same time \[\* S1\]. OpenAI has confirmed none of this \[not found\]. Rudzki's own closing line is the one to keep: "This system is still being rebuilt week to week" \[✓ S1\].

Now read that as a marketer who pays a tool to ask ChatGPT "best trading app for beginners in India" every morning and record whether your app is in the answer. Two things can move that number. One is the engine, which you cannot see and cannot steer, and which is now known to be running experiments on slices of its own traffic. The other is the list of prompts you typed into the tool on day one, which you can see and control completely.

This piece is about the second thing. Our view, stated plainly because a guide should have one, is that the prompt list is the instrument. Change the list and the number changes, and from then on you cannot tell whether it was you or the engine. So the list is written once, with care, and then left alone. The example throughout is a stock broking app in India, and the sources are what the tracking companies have published and the places where they fall silent.

## Hold the list still

Every tracking tool starts the same way. You type your brand and your website, and it offers to write your prompts for you. Table 1 is what six of them hand you at the door, on the pages we read on 21 September.

**Table 1: What six tools do with your prompts at setup (pages read 21 September 2026 unless stated)**

| Tool     | Prompts on the entry paid plan                                                                  | Who writes the prompts, on the vendor's pages                                                                                              | Refresh                                                |
| -------- | ----------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------ |
| Peec     | 50 on Starter \[\* S9\]                                                                         | Suggestions "drawn from your brand context and coverage gaps"; prompts from keywords; each prompt classed branded or non-branded \[\* S9\] | Daily \[\* S9\]                                        |
| Profound | No paid entry tier on the page; a free trial of 10 prompts run once, then Enterprise \[\* S10\] | Trial users get a recommended set for their industry and cannot change it; Enterprise gets a "tailored prompt tracking plan" \[\* S10\]    | One time on trial; daily on Enterprise \[\* S10\]      |
| Otterly  | 15 on Lite \[\* S11\]                                                                           | Its keyword research tool turns keywords into prompts, ranked by a Relevancy Score \[\* S7\]                                               | Daily \[\* S11\]                                       |
| Semrush  | 25 for small teams in the AI Visibility Toolkit; 500 to 15,000 on enterprise \[\* S3\]          | Prompt Research draws candidates from a clickstream database; Enterprise AIO turns each topic into roughly 20 prompts \[\* S3\]            | Daily \[\* S3\]                                        |
| Scrunch  | 350 custom on Starter, beside 1,000 "industry prompts" \[\* S12\]                               | You write the custom ones; Scrunch supplies the industry ones; three personas \[\* S12\]                                                   | not disclosed on the page read                         |
| Pallix   | 30 on Starter (release of 9 August) \[\* S13\]                                                  | "the prompts your customers actually type", supplied by you \[\* S13\]                                                                     | Every two weeks on Starter, daily on Growth \[\* S13\] |

Take the offer. Then tag every prompt the tool gives you as tool-generated, because in three months you will want to know how much of your number rests on questions a vendor guessed. Scrunch's entry plan puts a thousand prompts it wrote beside the 350 you write, and a share of voice built on that mix is mostly Scrunch's idea of your market \[\* S12\]. Semrush's own clickstream study found that the single most common ChatGPT prompt in its US sample was one ChatGPT had suggested to its users \[\* S4\]. We would not want to be the marketer whose tracked set turned out to be the same thing, one step removed \[\~ ours\].

Then the rule this whole piece hangs on. A prompt, once it is tracking, is never edited. If you want different words, that is a new prompt with a new start date, and the old one is retired with an end date and its history kept. Peec's researchers arrived at the same rule from the data. Their study of 37,804 responses across five engines found brand mentions holding steady while a reworded prompt stayed close in meaning, then falling by about half once the wording had drifted far enough \[\* S5\]. Their instruction: "If a core qualifier changes, treat it as a new intent" \[✓ S5\]. From inside a dashboard the drift is invisible. All you see is a number that moved, and no way to say why.

So every prompt carries tags from the day it is added, and the tags are what let you read the number later. Table 2 is the set we would use.

**Table 2: The tags on every prompt**

| Tag             | Values                                                     | Why it is there                                             |
| --------------- | ---------------------------------------------------------- | ----------------------------------------------------------- |
| Phase           | Awareness, consideration, evaluation, purchase             | Decides which bucket the answer feeds                       |
| Source          | Your head, your data, Google's data, a community, the tool | Shows how much of the number rests on guesses, and whose    |
| Brand in prompt | Yours, a competitor's, none                                | Keeps branded answers out of the visibility number          |
| Style           | Conversational or search-shaped                            | The two styles pull different numbers of brands (see below) |
| Language        | English, Hindi, Hinglish                                   | Left to you; we do not set a share                          |
| City            | A named city, or none                                      | So the geography question can be tested rather than assumed |
| Added, retired  | Dates                                                      | The audit trail. A prompt is never rewritten                |

One of those tags needs a word. Peec's study found that a short, search-shaped prompt pulls up to a quarter more brand mentions than a conversational prompt with the same intent, and that opening with a persona ("You are an IT consultant") pushes engines toward an explanation with fewer names in it \[\* S5\]. Semrush's clickstream, which is US only, found the share of ChatGPT prompts written in search language nearly doubled between October 2025 and February 2026, from 18.9 to 34.9 per cent \[\* S4\]. Both styles are real, so both go on the list, each with its tag, and the two are never averaged.

## When your own name is in the question

A prompt with your brand in it cannot measure whether you get named. It will name you. Ask "is Groww safe" and Groww is in every answer, and a tool that adds that prompt to your visibility score has just handed you points for free. Peec's January guide makes the same point, and its product now classes every prompt as branded or non-branded \[\* S2, S9\].

So the list splits before you write a word. Non-brand prompts make the visibility number. Brand prompts go into two other buckets and stay there. Sentiment, for what the engines say about you when asked directly: "is Groww safe", "Zerodha vs Groww for a beginner". Purchase, for where they send someone who has already decided: "how to open a Groww account". Three numbers sit side by side on the page. They are never added together.

Competitor-named prompts are the awkward case. "Zerodha alternatives" has no Groww in it, so for Groww it is non-brand and belongs in the visibility set. But it guarantees Zerodha a seat in the answer, so your share on those prompts will read lower than on a plain category prompt. Keep them in the set, tag the competitor's name, and expect the lower reading.

## A first-timer in Indore, a trader on expiry day, an NRI in Dubai

Say you market a broking app. We will use real broker names inside the prompts and no name for you, because the standard we write to bans inventing what a real company's team did.

Semrush's guide says constraints "push AI systems out of explanation mode and into recommendation mode" \[✓ S3\], and that a generic persona produces a generic prompt, which gets the big names back. For broking that is the whole game. "Best trading app in India" is the generic prompt. "Best trading app for a first-time investor in Indore who wants to fund by UPI and pay no yearly account charge" is a different instrument, and we would expect the list it returns to change with each constraint added. We have not run it, and we say so \[\~ ours\].

Three people to write for. A first-time investor in a tier-two city, funding by UPI, put off by an annual maintenance charge, asking whether any of this is safe. An active options trader who cares about the charge per order and whether the app holds up on a volatile morning, and who will ask about outages by name. A non-resident in Dubai who first needs to know who will open an account for them at all, and then what the paperwork looks like. Each gets constraints written in their own words, and each is a tag.

One thing constraints do that no tool will warn you about. Peec's study found that in ChatGPT and Perplexity, adding a budget or feature constraint reduced the number of brands in the answer, while in Gemini and Google AI Overviews it increased them \[\* S5\]. The same prompt is a different instrument on each engine. Report the engines separately before you blend anything.

Then geography. Peec's guide says the same prompt gets different answers by country and gives a UK-versus-US example for one bank \[\* S2\]. Pallix says it runs its prompts from India-weighted addresses so an Indian brand is measured against Indian answers \[\* S13\]. Nobody, that we could find, has published whether Lucknow gets a different broker list from Mumbai for the same prompt \[not found\]. Our view is that you test it before you pay for it. Run three prompts with a city in them beside the same three without, for a month, and look. If the lists match, you have a third of your prompt budget back.

## Four questions, unequal weights

A person choosing a broker asks four kinds of question, in an order. Whether to invest through an app at all. Which app. Whether this app. How to open the account. The first two carry no brand and feed the visibility number. The last two carry a brand and go to the sentiment and purchase buckets from the section above. For a broking app they look like this.

Awareness: "is it safe to invest in stocks through an app in India". Consideration: "which trading app has the lowest charges for options in India". Evaluation: "Zerodha vs Groww for a beginner". Purchase: "documents needed to open a Zerodha account as an NRI".

Peec's guide keeps the awareness phase. Semrush's drops it and tracks only prompts where the engine weighs options and names a winner \[\* S2, S3\]. We keep it, because an awareness answer that names one broker as the safe exception is worth more to you than a consideration answer that names five. But we do not weight the phases equally. Our split, and it is a judgment: a quarter of the list on awareness, a third on consideration, a quarter on evaluation, and the rest on purchase \[\~ ours\]. Purchase answers are instructions, and they barely move. Consideration is where you are named or not, and where the competitor-named prompts live, so it gets the most. Peec's study came to a similar tilt from its own data, suggesting half of a tracking budget on unbranded middle-of-funnel prompts because that is where the wording decides who appears \[\* S5\].

After thirty days, look at which phase moved and shift the list toward it. That is the one change the rule allows, and it is done by adding and retiring, never by rewriting.

## Where the questions come from

The tools will tell you they know what people type. Otterly, to its credit, says on its own blog that no major AI platform provides search volume and that its Intent Volume is modelled from Google keyword data: "No, it's not entirely accurate, but it provides a reliable estimate" \[✓ S7\]. Semrush's clickstream is the largest real sample we have seen, and it is a US panel; in it, between 65 and 85 per cent of ChatGPT prompts matched no keyword in a database of 27 billion \[\* S4\]. Google's India blog says early testers of AI Mode here asked questions two to three times the length of a search \[\* S8\]. Keyword data, then, gives you the problems people have and the words they use for them, and stops there.

Three piles to dig in. Your own: the non-brand queries in Search Console, and the questions your support desk answers every day. Google's public data: People Also Ask, Trends, and what AI Mode itself says when you ask it. Communities: r/IndianStockMarket and r/IndiaInvestments, app store reviews, the comments under finfluencer videos, X on any morning an app goes down, and TradingQ&A, which Zerodha launched in 2014 and where Zerodha's moderators answer alongside traders, so read it knowing that \[✓ S14\].

One rule for turning what you find into a prompt. Take the problem in the person's own words. Keep the constraint. Drop the brand if the prompt is going into the visibility set. Then rewrite it as a question someone would type to an assistant rather than to a search box.

Here is the rule applied to one real complaint. On 21 June 2024 Zerodha's platform froze for a short period during trading, and Business Standard quoted a user on X: "Who will take responsibility for my loss due to Zerodha's freeze?" \[\*p S15\]. Four prompts come out of that one line. Awareness, non-brand: "what happens to my open positions if my trading app freezes during market hours in India". Consideration, non-brand: "which trading app in India is least likely to freeze at market open". Evaluation, brand: "does Zerodha still freeze on volatile market days". Purchase, brand: "how do I transfer my demat holdings from Zerodha to another broker". Each carries the tags from Table 2: source, community via press; language, English; city, none; brand in prompt, none, none, Zerodha, Zerodha; style, conversational; added 21 September 2026, and, if one of them is ever retired, a date for that too.

Two caveats belong here. "Predicting" what people will type is too strong a word for any of this. You can get closer. You cannot know. And a complaint is a question about a problem, so a list built only from complaints leans toward awareness, while Semrush's US data suggests the prompts that look like purchases are the short, search-shaped ones \[\* S4\]. The phase split in the section above is there to catch that lean.

## What nobody has measured

No engine publishes the prompts people type. That is the ground every other silence in this piece stands on. Semrush's sample is a US panel \[\* S4\]. Profound's country list for its Prompt Volumes product, when we read it on 13 September, had ten countries, and India was not one of them \[\* S16\]. Pallix's launch release says you supply the prompts; its FAQ, on our 13 September read, described prompts built around "brand, category, market and buyer journey", which is a different answer to the question of who writes them \[\* S13, S17\].

Nobody has run the same broking prompt from two Indian cities and published what came back \[not found\]. Nobody has published a test of Peec's cover-the-funnel method against Semrush's bottom-of-funnel method with the brand lists side by side \[not found\]. Peec's study warns that regulated categories may behave differently because of engine guardrails, a caveat it attaches to its own 18 sub-verticals \[\* S5\]. Broking is regulated. A prompt that asks which app to trade options on may get a paragraph about risk before it gets a name, and we have seen no study of how often that happens \[not found\].

And the pages move. Profound's pricing page, when we read it on 13 September, listed a Starter tier at 50 prompts and a Growth tier at 100 \[\* S10\]. On 21 September the same address listed a free trial of ten prompts run once and an Enterprise plan, and nothing in between \[\* S10\]. We will ask Profound what happened to the tiers and add its answer here. Semrush's prompt-research guide carries a date of 17 August 2026 on the live page; a search engine's stored copy of the same address, seen the same day, carried 3 February 2026 and a different contributor's name \[✓ S3\]. Every entry in Table 1 will read differently in a month, which is the case for holding your own list still, made for us by the people who sell the tracking.

## The dates

Here is what we will come back to. Peec's report says the ChatGPT index may look different by the time anyone reads it, so we will re-read it on 4 October, a month on, and note what has changed \[\* S1\]. Profound's reply on its tiers, if one comes. The Semrush guide, in case its date moves again. The SSRN paper behind Peec's study, which we have not yet opened; what we have read is the sponsored summary its authors wrote for Search Engine Journal \[not read, S5\]. And one question the record cannot answer yet: whether an engine that is running five experiments on what it shows a shopper will, by the time you have thirty days of data, still be the engine you started measuring.

## Labels used in this article

Every number and quotation carries a mark and a source number. The key:

| Mark          | Label             | Meaning                                                                                                                                                                                                                                  |
| ------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ✓             | Verified          | Read on the primary page (a company's own post or documentation, a decision, a study) and a documented fact, a dated change we observed ourselves, or the company's own words in quotation marks. Not a self-description of performance. |
| \*            | Claimed           | A company asserts it about itself, its product, its data or its users, including a study it ran or paid for, and we read it on the company's own page.                                                                                   |
| \*p           | Claimed via press | The same, but read in a third party's account.                                                                                                                                                                                           |
| \~ ours       | Inferred (ours)   | Our own reading, judgment or derivation, with the basis stated. Never upgraded to Verified.                                                                                                                                              |
| not disclosed |                   | The company does not say, on the pages checked.                                                                                                                                                                                          |
| not found     |                   | No source was located.                                                                                                                                                                                                                   |
| not read      |                   | A source exists but we have not opened it.                                                                                                                                                                                               |

## Sources

All pages captured 21 September 2026 unless stated.

S1\. Peec AI blog, "ChatGPT built its own search index", Tomek Rudzki, 4 September 2026\. [https://peec.ai/blog/chatgpt-built-its-own-search-index](https://peec.ai/blog/chatgpt-built-its-own-search-index?ref=salienceindex.com)

S2\. Peec AI blog, "How to choose the right prompts for LLM tracking", Tomek Rudzki, 22 January 2026\. [https://peec.ai/blog/how-to-choose-the-right-prompts-for-llm-tracking](https://peec.ai/blog/how-to-choose-the-right-prompts-for-llm-tracking?ref=salienceindex.com)

S3\. Semrush blog, "How to do prompt research for AI SEO", Sergei Rogulin, live page dated 17 August 2026 (contributor Faizan Ali). A search engine's stored copy of the same address, seen 21 September 2026, was dated 3 February 2026 (contributor Cecilia Meis). [https://www.semrush.com/blog/prompt-research-for-ai-seo/](https://www.semrush.com/blog/prompt-research-for-ai-seo/?ref=salienceindex.com)

S4\. Semrush blog, "ChatGPT traffic analysis: Insights from 17 months of clickstream data", Luke Harsel, 7 April 2026\. US clickstream, October 2024 to February 2026, 200 million-user panel. [https://www.semrush.com/blog/chatgpt-search-insights/](https://www.semrush.com/blog/chatgpt-search-insights/?ref=salienceindex.com)

S5\. Search Engine Journal, "What Matters In An AI Prompt? Intent or Keywords?", Malte Landwehr (Peec AI), 15 June 2026, modified 6 July 2026; marked as sponsored by Peec AI. Reports Ehrlinspiel, Landwehr and Rudzki, "Prompt Tracking Works, But Not as a One-Prompt Measurement System", SSRN 6914539, 10 June 2026, which we have not read. [https://www.searchenginejournal.com/ai-prompt-intent-keywords-peec-spa/576201/](https://www.searchenginejournal.com/ai-prompt-intent-keywords-peec-spa/576201/?ref=salienceindex.com)

S6\. SparkToro, "NEW Research: AIs are highly inconsistent when recommending brands or products; marketers should take care when tracking AI visibility", Rand Fishkin with Patrick O'Donnell of Gumshoe, 27 January 2026, modified 28 June 2026\. Read for background on run-to-run variance; not cited for a number in this piece. [https://sparktoro.com/blog/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers-should-take-care-when-tracking-ai-visibility/](https://sparktoro.com/blog/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers-should-take-care-when-tracking-ai-visibility/?ref=salienceindex.com)

S7\. Otterly.AI blog, "Intent Volume & Relevancy Score Explained", undated (the text says "As of May 2025"), read through Otterly's ai-cache mirror. [https://ai-cache.otterly.ai/blog/intent-volume/index.html](https://ai-cache.otterly.ai/blog/intent-volume/index.html?ref=salienceindex.com)

S8\. Google India Keyword blog, "Google Search: Introducing AI Mode in India", Hema Budaraju, 24 June 2025\. [https://blog.google/intl/en-in/products/google-search-introducing-ai-mode-in-india/](https://blog.google/intl/en-in/products/google-search-introducing-ai-mode-in-india/?ref=salienceindex.com)

S9\. Peec AI, Pricing, undated. [https://peec.ai/pricing](https://peec.ai/pricing?ref=salienceindex.com)

S10\. Profound, Pricing, undated. Captured 13 September 2026 (Starter, 50 prompts; Growth, 100 prompts; Enterprise) and again 21 September 2026 (Trial, 10 prompts run once; Enterprise). [https://www.tryprofound.com/pricing](https://www.tryprofound.com/pricing?ref=salienceindex.com)

S11\. OtterlyAI help centre, "What are the plans and the pricing of OtterlyAI?", undated. [https://help.otterly.ai/pricing-of-otterlyai](https://help.otterly.ai/pricing-of-otterlyai?ref=salienceindex.com)

S12\. Scrunch, Pricing, undated. [https://scrunch.com/pricing](https://scrunch.com/pricing?ref=salienceindex.com)

S13\. Pallix, launch press release, vendor-issued via 24-7 Press Release, 9 August 2026\. [https://www.24-7pressrelease.com/press-release/537568/pallix-launches-an-ai-visibility-tool-built-for-india-tracking-what-chatgpt-perplexity-and-google-ai-tell-buyers](https://www.24-7pressrelease.com/press-release/537568/pallix-launches-an-ai-visibility-tool-built-for-india-tracking-what-chatgpt-perplexity-and-google-ai-tell-buyers?ref=salienceindex.com)

S14\. Zerodha support, "What is TradingQ&A?", undated; Zerodha Z-Connect, "Trading Q&A", 31 March 2014\. [https://support.zerodha.com/category/trading-and-markets/trading-faqs/general/articles/what-is-tradingqna](https://support.zerodha.com/category/trading-and-markets/trading-faqs/general/articles/what-is-tradingqna?ref=salienceindex.com) and [https://zerodha.com/z-connect/general/tradingqna](https://zerodha.com/z-connect/general/tradingqna?ref=salienceindex.com)

S15\. Business Standard, "Zerodha faces another tech snag; users complain for losing money in trades", 21 June 2024\. [https://www.business-standard.com/markets/news/zerodha-faces-another-tech-snag-users-complain-for-losing-money-in-trades-124062100374\_1.html](https://www.business-standard.com/markets/news/zerodha-faces-another-tech-snag-users-complain-for-losing-money-in-trades-124062100374%5F1.html?ref=salienceindex.com)

S16\. Profound, "Prompt Volumes" features page, undated, captured 13 September 2026; not re-read on 21 September. [https://www.tryprofound.com/features/prompt-volumes](https://www.tryprofound.com/features/prompt-volumes?ref=salienceindex.com)

S17\. Pallix, FAQ, undated, captured 13 September 2026; not re-read on 21 September. [https://pallix.in/faq](https://pallix.in/faq?ref=salienceindex.com)