Get the FAQ Out!

Bloated FAQ pages are killing your UX. Here’s what to do instead.

Introduction

There’s a gold rush happening. As LLMs like ChatGPT, Perplexity, and Claude reshape how people discover and interact with content, digital teams are scrambling to load up their sites with exhaustive FAQs to boost Large Language Model Optimization (LLMO). And look, we get it: more structured questions can increase the chance of being cited by LLMs. 

But FAQs are a nightmare for human users who just want quick answers.

It doesn’t have to be this way. You can serve both audiences. You just need to separate the content layer for humans (UX) from the one for machines (MX).

The problem with the FAQ strategy for LLMO

If anyone tells you to “just make a big FAQ section and add FAQPage schema,” please, don’t. This brute-force method leads to pages with tons of dropdowns, TL;DR text blocks, and cognitive overload. And it doesn’t even guarantee better LLM performance.

Why it fails:

• Humans scan. FAQs force reading.

• Question-based content can’t front-load keywords.

• Schema.org markup enforces a rigid format of question-answer pairs. This isn't well-suited for nuanced explanations or interrelated topics.

• From the machine side, this limits the semantic richness that LLMs rely on to form human-like responses.

Neither audience gets what it really needs.

You can try to manage LLMO manually, but it's time-consuming, hard to maintain, and nearly impossible to scale well. There’s a better way to serve both your human users and the AI models indexing your site.

Introducing Machine Experience (MX)

We coined this term because you're no longer optimizing for human search intent only. You’re optimizing for how LLMs crawl, digest, and cite your content.

UX is for humans.
MX is for machines.
You need both.

LLMs don't get tired. They can parse 500 FAQs. In fact, they benefit from quantity, as long as it's well-organized and properly formatted.

This means your website can, and should, have far more content than you surface directly to users. But it needs to be indexed in a way that machines can reliably find and interpret.

The trick is making that layer accessible without sacrificing design, usability, or SEO compliance. That's where AI Findr comes in.

AI Findr: Your dual-experience layer

AI Findr is an AI-powered, customizable search engine that decouples user experience from machine readability. With AI Findr, users experience a single, conversational search bar fed by a centralized knowledge base of your company’s products, rules and content.

Meanwhile, our sister product LLM Findr works in the background to boost your visibility across major LLM platforms, by structuring the same content into the format machines prefer (JSON-LD, structured data, and llms.txt). 

How JSON-LD, structured data, and llms.txt, work together:

Structured data: Information formatted for machines using standardized schemas like Schema.org

JSON-LD: Embeds the structured data directly into your HTML

llms.txt: A plain-text file (like robots.txt) that lives at the root of your domain (e.g., yourdomain.com/llms.txt) and points LLMs to your structured content 

Wait, is it kosher to serve different content to humans and machines?

Yes, if you do it right. What you want to avoid is cloaking. 

What is cloaking? 

Cloaking is a black-hat SEO tactic where the goal is to trick crawlers (like Googlebot) into indexing content that is different from what users see: often keyword-stuffed, low-quality pages. This violates search engine guidelines because it manipulates visibility based on misrepresentation.

LLM Findr, on the other hand, is about optimization through alignment and transparency. Here's how it's different:

LLM Findr doesn’t smuggle in content, it just renders it differently based on audience needs. The UX surface is simplified to reduce cognitive load. The MX layer is structured, machine-readable, and fully aligned with what’s publicly available.

So I should replace my FAQs with AI search for better UX?

Yes, absolutely. AI Findr provides a familiar search interface, but with AI-enhanced abilities to understand your users’ language, intent, and emotions. 

The results speak for themselves.

The following metrics come from live pilots with our clients:

• 15% average increase in sales

• 52.6% more fully digital sales—no human salesperson intervention

• 200% improvement in CSAT from self-serve customer support

• 39 point average increase in NPS

Conclusion

As AI-generated answers become more influential in buyer journeys, controlling how your brand shows up in those answers becomes mission-critical. 

Bigger FAQs aren’t the answer. Separate experience layers are.

AI Findr helps you build a layered content architecture that works for both humans and LLMs.

Get the FAQ out of your UI. Let AI Findr handle the rest.