Showing Up in LLMs Results: 7 Things We've Learned

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Entrepreneurs and content creators are racing to crack LLM optimization as AI search fundamentally reshapes discovery, with companies like Tally growing from $2M to $3M ARR in four months and Vercel now getting 10% of signups from ChatGPT alone.

This research synthesizes insights from hundreds of real discussions across Reddit, Hacker News, IndieHackers, Stack Overflow, and specialized blogs to reveal what actually works when you want to get cited by LLMs.

The emerging discipline differs fundamentally from traditional SEO because while Google rewards backlinks and keyword density, LLMs prioritize content clarity, authentic community presence, and structural accessibility (which is exactly the kind of insight we uncover in our market clarity reports when analyzing digital product markets).

Learning	Details
Reddit gets 40% of citations	Reddit accounts for 40.1% of all LLM citations (nearly double Wikipedia's 26.3%), but you need authentic engagement, not promotional posts. Follow a three-phase approach: weeks 1-3 only comment, weeks 3-5 use 80/20 value-to-promotion ratio, only after week 5 post original threads.
AI traffic converts differently	AI visitors show 6x better signup rates but 5x worse paid conversion than organic search. They're discovering your product before they're ready to buy, so optimize for awareness rather than immediate sales.
JavaScript blocks AI crawlers	Most AI crawlers can't execute JavaScript (only Google Gemini and AppleBot can). If your content loads via React, Vue, or Angular without server-side rendering, it's invisible to LLMs. Even JSON-LD added through Google Tag Manager won't work.
Comparison tables get cited more	"[Product] Alternatives" pages with comparison tables in the first 200 words get cited dramatically more than guides or lists. LLMs can directly extract and reformat table data to answer user questions like "Which CRM is better for small teams?"
Conversational writing wins by 80%	Content written conversationally gets cited 80% more than formal business writing. LLMs are trained on Reddit and Quora, not corporate marketing. Write like you're explaining to a colleague, use "you" instead of "users," and answer questions directly.
Schema markup triples citations	Valid structured data (FAQ and HowTo schemas especially) increases citations by 3x. But it must be implemented server-side using JSON-LD format, not through Google Tag Manager. FAQ schema alone can boost visibility by 31%.
ChatGPT rarely searches the web	ChatGPT only uses web search for 30-35% of queries. The other 65-70% comes from training data. You need two strategies: get mentioned on Reddit, Wikipedia, and Stack Overflow for training data, and optimize for Bing for the queries that trigger web search.

You get better LLM results by prioritizing Reddit above all other platforms

We have learned from multiple studies that Reddit dominates LLM citations with 40.1% frequency, nearly double Wikipedia's 26.3%, but you can't just drop promotional links and expect results.

Multiple studies confirm Reddit as the single most-cited source across ChatGPT, Perplexity, and Google AI Overviews, accounting for 21-40% of all citations depending on the platform. Google's $60M annual partnership with Reddit feeds content directly into training data, while Reddit's visibility in Google searches jumped 2,100% in eight months.

The successful approach follows a three-phase strategy where you spend weeks 1-3 only commenting and upvoting without brand mentions, building karma and trust. In weeks 3-5, you follow an 80/20 rule with 80% pure value and 20% natural brand mentions using transparent usernames, then only after week 5 post original threads.

This works because LLMs learn what real people think from Reddit, making authentic presence more valuable than any traditional advertising spend.

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You see different LLM results conversions with 6x signups but 5x worse paid

Real company data shows us that AI-referred visitors show 6x better signup rates but 5x worse paid conversion, revealing a critical distinction between discovery intent and purchase intent that most companies miss.

Real company data from Sentry reveals that LLM traffic represents 10% of Google's volume but produces six times higher website signups. Yet these same users convert to paid customers at five times lower rates than organic search visitors, and Seer Interactive found ChatGPT traffic converts at 15.9% versus Google's 1.76% but with 30% lower session duration.

This pattern makes sense when you understand user psychology because people discover brands through conversational AI exploration ("What are good form builders?") before they're ready to buy, creating highly qualified awareness without immediate purchase intent. In contrast, Google searchers often arrive with specific buying intent after they've already done extensive research elsewhere.

Here's what this means for you: optimize for AI to own the discovery phase, but don't expect immediate sales or you'll be disappointed by your conversion metrics.

You lose LLM results visibility if your content requires JavaScript execution

Technical research analyzing millions of crawler requests reveals that most AI crawlers fail to execute JavaScript, meaning only Google Gemini and AppleBot render it, which makes client-side content effectively invisible to the LLMs that matter most.

Technical research analyzing millions of crawler requests reveals that GPTBot, ClaudeBot, PerplexityBot, and other major AI crawlers can't execute JavaScript and only access the initial HTML your server sends. Content loaded via React, Vue, Angular, or other JavaScript frameworks stays completely invisible unless you implement server-side rendering, and here's a particularly damaging discovery: JSON-LD structured data added through Google Tag Manager is invisible to AI crawlers since GTM injects code client-side after page load.

The solution requires architectural changes like implementing SSR through Next.js or Nuxt, using prerendering services like Prerender.io, or reverting critical content to static HTML. For structured data specifically, you need to inject JSON-LD directly into server-rendered HTML rather than through tag managers (the kind of technical implementation detail that can make or break your visibility in AI search, similar to the distribution insights we cover in our market clarity reports).

This isn't going away anytime soon because it reflects how AI companies prioritize speed and security over full browser emulation, meaning this competitive advantage will stick around for years.

You boost LLM results dramatically by structuring content as comparison tables

We have seen across multiple case studies that comparison and "alternatives" content with tables at the beginning gets cited dramatically more than comprehensive guides or feature lists because of how LLMs process structured information.

Tally achieved 70% visibility rate for "free form creator" queries primarily through comparison content like "Jotform Alternatives" and "Best Free Online Form Builders in 2025." The pattern appears across platforms where ChatGPT favors side-by-side comparison tables with clear verdicts ("Best for Enterprise" versus "Best for Startups"), while Perplexity cites 35% listicles in general queries and 91% list-formatted content for YMYL topics.

Research from Microsoft shows HTML-formatted tables outperform CSV/TSV by 6.76% for LLM understanding, and comparison tables give LLMs exactly the structured format they need to answer user questions. The winning formula: create "[Competitor] Alternatives" pages with comparison tables in the first 200 words, include specific verdicts for different use cases, use clear column headers that match common search criteria like pricing and features, and update regularly with current dates.

Here's why it works: users ask LLMs comparative questions like "Which CRM is better for small teams?", and LLMs can directly pull data from your tables and reformat it into answers, while generic guides without clear structure get ignored.

Each of our market clarity reports includes a study of both positive and negative competitor reviews, helping uncover opportunities and gaps.

You increase LLM results by 80 percent writing conversationally versus formally

A revealing experiment shows us that content written in conversational question-answer format significantly outperforms formal business writing for LLM citations, with one test showing 80% higher citation rates for the conversational version.

A revealing experiment by an SEO practitioner tested identical content in two styles, formal business writing versus conversational Q&A format, and ChatGPT cited the conversational version 80% more frequently. This aligns with how LLMs are trained because they learn primarily from conversational platforms like Reddit, Quora, and community forums (which accounts for 40% of citations) rather than corporate marketing materials.

Here's how to actually do this: instead of "Our platform provides comprehensive solutions for enterprise workflow management," write "Tool X is the best choice for teams on a budget that need features like multi-user logins and grammar checking." Use question-based H2 headers ("How does Perplexity rank websites?" not "Perplexity Ranking Algorithm"), answer immediately in the first sentence after each heading, and write in second person ("you" not "users" or "companies").

LLMs are built for conversation, so content that reads like a conversation gets retrieved and cited way more effectively than keyword-stuffed marketing copy.

You multiply LLM results by three times when implementing proper schema markup

Multiple sources confirm that valid structured data, especially FAQ and HowTo schemas, increases citation rates by up to 3x according to practitioner testing and enterprise studies.

Practitioner testing found content with proper schema markup gets referenced three times more often than identical content without it, and a 2024 Google study showed pages with valid structured data were 27% more likely to appear in AI Overview panels. Controlled experiments found that well-implemented schema appeared in AI Overviews and ranked for 6 keywords (best position 3), poorly-implemented schema ranked for 10 keywords but never appeared in AI Overviews, while pages with no schema weren't indexed by Google at all.

Here's what you need to do: use JSON-LD format (Google's recommended method) rather than microdata, implement server-side (not through Google Tag Manager which AI crawlers miss as we discussed earlier), and focus on FAQ, HowTo, Article, and Product schemas as priority. The most impactful schema types: FAQ schema for question-answer content (boosts visibility by up to 31%), HowTo schema for instructional content, Product schema with reviews and pricing for commercial content, and Organization/Person schema for entity recognition (this is the kind of technical optimization detail we validate through real customer complaint analysis in our market clarity reports to make sure implementations actually move the needle).

Schema doesn't guarantee citations, but it seriously improves your odds by giving LLMs clearly labeled, machine-readable context about what your content means and how it's structured.

You optimize LLM results differently knowing ChatGPT only searches 30 to 35 percent

Seer Interactive's FLIP Framework research reveals that ChatGPT uses web search for just 30-35% of queries while the rest rely entirely on training data, requiring a completely different optimization strategy than most marketers realize.

Seer Interactive's FLIP Framework research reveals that ChatGPT relies on pre-trained knowledge for 65-70% of queries and only triggers web search for queries needing Freshness, Local intent, In-depth context, or Personalization. This explains why some websites with strong Google rankings never show up in ChatGPT: if queries about their topic don't trigger web search, their real-time content doesn't matter.

The tactical approach requires splitting efforts between two distinct goals where for training data presence (the 65-70%) you focus on getting mentioned on platforms LLMs train on like Reddit, Wikipedia, Stack Overflow, and academic papers, while for web search visibility (the 30-35%) you optimize for Bing since ChatGPT uses it as primary backend with 73% correlation. You also need to add temporal modifiers to content like dates and "2025 update," create location-specific content for local queries, and provide depth that requires real-time sourcing.

Here's the thing: most companies over-invest in web search optimization while completely ignoring training data presence, when training data actually determines the vast majority of their potential visibility in ChatGPT responses.

In our market clarity reports, for each product and market, we detect signals from across the web and forums, identify pain points, and measure their frequency and intensity so you can be sure you're building something your market truly needs.