Elevator Talk: Should You Optimize Content Differently for ChatGPT, Gemini, and Claude?

Great question! I too have been treating them the same, despite some of my clients having massive skew towards a specific LLM. 100% worth digging into more!

I would love to set up some tests/an experiment for those audiences that are over-indexing on a specific LLM. Maybe picking a topic for one of them and doing something like:

• Analysis of what's getting cited on one and not another
• Optimizing with the intention of showing up for the “priority” LLM
• Tracking progress on all and seeing if there's a bigger lift on just the one, or if it impacts all

My gut is saying that optimizing for one will optimize for all, but it'd be cool to back that up with real data!

Honestly, I've been treating them the same too. My hunch is there's probably real overlap in the fundamentals (clear structure, direct answers, credible sourcing, strong E-E-A-T signals), but each assistant likely has its own logic for what it chooses to surface and cite. Some things I've been thinking about:

• When I'm optimizing a piece for “AI visibility,” am I actually optimizing for a specific assistant's behavior, or just a generic idea of “AI-friendly content”?
• Are there signals I'm treating as universal that might actually only matter to one of them? If so, what?

Like Rachel mentioned, I think testing & sharing results will be super valuable in helping us answer some of these questions.

I came across this article from Yext; they analyzed 17.2 million AI citations across ChatGPT, Perplexity, Gemini, and Claude, and it actually gets at exactly what we're talking about. Turns out each one retrieves differently: Gemini leans on Google's search index, Perplexity is pretty stable across industries, and ChatGPT's retrieval varies a lot by industry. Claude's the real outlier though; it cites user-generated content (reviews, forums) at 2–4x the rate of the others, which the piece ties back to its Constitutional AI framework.

The part that stood out to me: across all four, verified, structured data still makes up the majority of citations. So maybe both things are true; the fundamentals do carry weight everywhere, but each assistant is still weighting different signals on top of that.

Which makes me even more curious about testing this for an audience that skews toward one platform, especially if that platform is Claude; if reviews/UGC matter that much more there, our approach for that audience might need to look different from what we'd do for Gemini or ChatGPT.

One actionable insight when optimizing for specific LLMs is understanding where the LLMs are pulling their citations from. Claude pulls most of their information from Brave (alternative search engine). Profound did a study and found that 80% of Claude results track back to Brave's top 10 results.

So doing SERP Analysis on Brave and using that as a pillar to optimize should then help you get seen on Claude more.

And this tool is how you submit URLs into Brave's index.

I made a tool — you can search your prompts across Claude, ChatGPT, and Gemini and see who's showing up as a brand mention, what content is most cited, and the common themes, differences, and gaps across LLMs. We can use it to compare and optimize accordingly. The first example I a suggests there's a lot of crossover, but also some key differences you might have missed otherwise.