u/Apprehensive_Egg_374

I wanted to understand something about Perplexity that I kept noticing as a user:

When you run the same kind of search more than once, how stable are the sources?

And if you tell Perplexity to use a certain kind of source, does it actually listen?

Quick disclaimer: I’m not affiliated with Perplexity, Fathom, Fireflies, or any of the companies mentioned here. I was just curious about how Perplexity chooses and changes cited sources.

So I ran a small test.

I used one category, AI meeting note tools, and tested five queries:

- best AI meeting note taker for sales calls

- Fathom AI review

- Fathom vs Fireflies

- Fireflies AI review

- Fireflies vs Fathom

For each query, I ran five source conditions:

- normal baseline

- use official pages

- use reviews

- use Reddit / user discussions

- avoid listicles

I repeated each query + condition three times, so 75 searches total.

Then I collected and classified every cited URL: 926 citations.

The short version:

Perplexity can be nudged, but it cannot really be controlled.

The clearest result was Reddit.

When I asked Perplexity to use Reddit or user discussions, it changed the source mix on all 5 queries.

But the other instructions were much weaker:

- “use Reddit” worked on 5/5 queries

- “use official pages” worked on 3/5

- “use reviews” worked on 0/5

- “avoid listicles” worked on 1/5

By “worked,” I mean the three runs under that instruction moved outside the baseline range for the same query. I used that rule because Perplexity naturally varies between runs.

The default source mix also surprised me.

Across all 926 citations, Perplexity cited:

- vendor-owned editorial: 24.3%

- YouTube / creator videos: 14.1%

- third-party comparisons: 13.6%

- Reddit / forums: 12.4%

- third-party listicles: 11.2%

- official product pages/docs: 8.9%

- third-party reviews: 7.1%

- review platforms like G2/Capterra: 6.7%

So the normal answer was not mostly official pages or review platforms. It leaned heavily on vendor-written comparisons, YouTube, third-party comparison pages, listicles, and Reddit.

The “use reviews” result was the strangest to me.

I expected it to pull in more G2, Capterra, hands-on reviews, and user review pages. Instead, it did not create a clean shift at all.

Part of the reason: the baseline was already full of review-like sources. Another part: “review” is not one clean category. Perplexity mixed review platforms, YouTube videos, review blogs, comparison pages, and vendor-written competitor reviews.

Across the test, 50 of the 116 written review articles Perplexity cited were written by vendors themselves.

The “avoid listicles” instruction was also interesting. It only clearly worked on the broad query where listicles dominated. But when listicles were removed, the answers got thinner.

Baseline answers cited about 13.5 sources per run on average. “Avoid listicles” dropped to 9.9.

That changed how I think about source instructions.

They are not a remote control. They are more like a diagnostic.

If “use Reddit” works, there is Reddit/community evidence available.

If “use official pages” works, there is crawlable official material Perplexity can use.

If “avoid listicles” makes the answer thinner, the non-listicle evidence layer may be weak.

If “use reviews” does not move anything, the answer may already be saturated with review-like sources.

Small caveat: this was one engine, one category, 75 searches, and a short time window. I would not treat the exact percentages as universal.

But the pattern was useful:

Perplexity seems easier to nudge toward an existing source layer than to force toward a source type that is weak, missing, or already saturated.

Have you noticed the same thing? When you ask Perplexity to use specific sources, does it actually change the citations, or mostly reshuffle what it was already going to use?

This started as a very normal shopping rabbit hole: I wanted to buy a cat water fountain.

I asked Perplexity because every review site had a different “best” pick, and I wanted a quicker way to compare options.

What surprised me wasn’t the product recommendation itself. It was the sources Perplexity used.

Even when I asked about one specific brand, Perplexity didn’t just rely on the brand’s own site. It pulled from review roundups, retailer pages, Reddit, YouTube, and other third-party sources.

So I turned it into a small test.

I ran 15 queries around one product category:

Query type	Example query	Brand visibility in Perplexity	Sources Perplexity used
Problem-aware	“why won’t my cat drink from a bowl?”	0/5	Vets, health sites, Reddit, pet-care blogs
Solution-aware	“best / quietest cat water fountain”	4/5	Review roundups, retailers, brand pages
Brand-aware	“[brand] review / vs competitor / alternatives”	5/5	Brand site + review sites + Best Buy + Reddit + YouTube

The brand site seemed useful for specs and official product details.

But the trust layer came from elsewhere: reviews, retailers, Reddit, YouTube, and comparison pages.

That made me think branded AI search is less “what does the brand say?” and more “what does the web say about the brand?”

Small caveat: this was just one category, 15 queries, one run each, so I wouldn’t treat it like a benchmark.

But I’m curious if other Perplexity users see the same thing:

When you ask about a specific brand or product, does Perplexity mostly trust the official site, or does it lean more on third-party sources?

update 6/27/2026

follow-up / data update:

A few comments here pushed me to go back and classify the cited domains, not just list them.

After excluding image-only sources, the dataset had:

• 62 unique cited domains
• 138 answer-level domain appearances
• 281 URL mentions
• 15 queries
• 2 engines: Perplexity + ChatGPT
• 1 run per query, so this is source/incentive mapping, not a benchmark

The main thing I learned: “third-party” was not one category.

The cited sources included:

• expert / health authorities
• Reddit / community threads
• affiliate-disclosed review media
• retailers
• competitor brand pages
• seller-owned advice blogs
• manufacturer / supplier content
• YouTube videos
• app-store / review signals

The sharpest update for me:

Third-party does not mean independent.

AI is not citing a neutral web. It is citing an incentive map.

I also looked specifically at the “advice-style” sources — the ones that looked like neutral reviews, comparisons, or guides rather than obvious stores / Reddit / vet pages.

Out of 16 advice-style sources:

• 11 had confirmed affiliate disclosures
• 2 were seller/manufacturer-owned advice content
• 1 had a site-level affiliate program
• 1 had changed or become unverifiable
• 1 had no visible product-commerce incentive I could verify

That doesn’t mean the commercial sources are automatically bad or useless. But it does mean the trust layer under an AI product answer is not neutral by default.

So the question I’d now ask is not only:

“Which sources did Perplexity cite?”

but also:

“What does each cited source want?”

One more note on stability, since a few people brought it up:

This PETLIBRO set was one run per query, so I’m not treating the exact 0/5, 4/5, 5/5 numbers as stable mention rates.

But I have tested repeatability in a separate experiment, and the pattern was not “everything changes every time.” It was more like core vs tail: a few sources kept showing up repeatedly, while the long tail moved around.

So for this PETLIBRO test, I’d separate two questions:

1. What kinds of sources does AI use for each query layer?
2. Which of those sources stay stable across repeated runs?

This post mostly answers #1.

I was shopping for a cat water fountain, got overwhelmed by recommendations, and just asked ChatGPT and Perplexity instead.

What surprised me: even when I asked about one specific brand, the AI didn’t only repeat the brand’s own pages. It pulled in Reddit, retailer reviews, YouTube, and review sites too.

So I ran a proper small test.

I used one real brand, PETLIBRO, as a public example and tested 15 pet-water-fountain queries across three buyer stages: problem-aware, solution-aware, and brand-aware. I ran each query once on Perplexity and once on Solution-aware, e.g. “best / quietest cat fountain”ChatGPT 5.5 thinking, then recorded the visible cited sources.

Here’s what stood out:

Query stage	Brand shown?	Who AI cited
Problem-aware, e.g. “why won’t my cat drink?”	0/5	Vets, health sites, Reddit, pet-care blogs
Solution-aware, e.g. “best / quietest cat fountain”	4/5	Review media, retailers, brand pages
Brand-aware, e.g. “review / vs / alternatives”	5/5	Brand site + review sites + Best Buy + Reddit + YouTube

The brand’s own site did show up, especially in ChatGPT.

But even on brand-aware queries, it was never the whole answer. Reviews, retailer pages, Reddit, YouTube, and third-party tests shaped the answer alongside the official site.

That changed how I think about AEO/GEO.

Optimizing the website still matters: crawlability, product pages, schema, comparison pages, clear claims, etc.

But for branded AI search, that’s only one layer.

I’d also want to know:

- Which review sites does AI repeatedly cite?

- Do retailer reviews show up?

- Does Reddit show up?

- Are there YouTube tests?

- Which caveats does AI repeat?

- Which attributes does AI assign to competitors instead?

- Where in the funnel does the brand disappear?

My takeaway:

A brand’s website makes claims. Third-party sources make those claims believable. AI seems to use both.

So even on your own branded queries, you don’t fully own the answer. AI assembles owned, earned, and community sources together.

Small caveat: this was 15 queries, two engines, one run each, visible citations only, so I’d treat it as an early signal, not a benchmark.

Anyone else tracking AI visibility seeing the same thing? Do your branded-query answers lean on third-party sources as much as your own site?

>6/27/2026 update

Small follow-up: I went back and classified the cited domains after a few people here pointed out the “neutral third-party” problem.

The interesting part: “third-party” was not one category.

In this dataset, the sources Perplexity/ChatGPT cited included:

- vet / health authority sources

- Reddit / community threads

- affiliate review media

- retailer pages

- competitor brand pages

- seller-owned advice blogs

- manufacturer / supplier content

- YouTube videos

- app-store/review signals

So the sharper takeaway for me is:

Third-party does not mean independent.

A brand page has one incentive. But a review roundup, retailer page, competitor blog, manufacturer guide, YouTube video, and Reddit thread all have different incentives too.

I also checked the “advice-style” sources specifically — the ones that look like neutral reviews, comparisons, or guides rather than obvious stores / Reddit / vet pages. Out of 16 advice-style sources, only one had no visible product-commerce incentive I could verify. The rest were affiliate-disclosed, seller-owned, manufacturer-owned, site-level affiliate, or unverifiable/page-changed.

That doesn’t mean those sources are bad or useless. But it does mean AI product answers are not built on a neutral web. They’re built on an incentive map.

This also made me think the audit question shouldn’t just be “which sources does AI cite?” but “what does each cited source want?”

I got tired of reading AEO/GEO advice that felt like guesswork, so I ran a small citation test myself.

Very small sample: 3 queries, across ChatGPT, Perplexity, and Gemini.

Queries:

- "best AEO tools 2026"

- "how to get cited by ChatGPT"

- "AEO vs SEO"

I recorded every source each engine cited.

A few things stood out:

1. Small sites showed up more than I expected

I expected mostly HubSpot / Conductor / big SEO sites.

But smaller sites like cite.sh, getairefs, lasso-up, and width.ai showed up alongside bigger domains.

Obviously this is too small to say domain authority "doesn't matter", but it made me question how much traditional SEO authority carries over into AI citations.

2. Exact-match intent is important

The pages that got cited usually matched the query very directly.

For example, a page specifically about "best AEO tools" was more likely to appear than a broad marketing page that only mentioned AEO in passing.

3. Format does matter

Pages with:

- a direct answer near the top

- lists or tables

- clear comparisons

- question-shaped sections

were easier for the engines to pull from.

Some responses reused those structures almost directly.

4. Citation behavior changed by model/mode

This was the weirdest part.

ChatGPT in fast mode gave me no sources. Switching to thinking mode produced citations.

Gemini's faster model gave me nothing in this test.

Perplexity cited sources every time.

So "does this engine cite you?" may depend a lot on the specific mode/model, not just the query.

Again: small sample, not a definitive study.

But it made me think AEO / GEO might be less about generic domain authority and more about being the most extractable, query-matched source for a specific answer.

Happy to share the raw sheet if anyone wants to sanity-check it.

Has anyone here tracked their own AI citations across ChatGPT / Perplexity / Gemini? Curious whether you're seeing the same thing, or if this changes a lot by industry.