How to Appear in LLMs Answers: Feedback from 100+ Users
Last updated: 15 October 2025
Get a full market clarity report so you can build a winning digital business
We research digital businesses every day, if you're building in this space, get our market clarity reports
LLM-referred traffic jumped 527% between January and May 2025, making AI search the fastest-growing discovery channel most creators still ignore.
We scraped through thousands of discussions across Reddit, IndieHackers, Hacker News, and specialized forums to find what actually works when you're trying to show up in ChatGPT, Perplexity, Claude, or Gemini answers.
The feedback from over 100 experts reveals a pattern: traditional SEO gives you 77% of the visibility you need, but the remaining 23% requires completely different tactics.
This article compiles every practical insight we found, the same way we compile market signals in our market clarity reports.
Quick Summary
Getting cited by LLMs requires understanding platform-specific preferences, fixing technical invisibility issues like client-side rendering, and creating content that balances being comprehensive enough to cite but deep enough that AI summaries feel insufficient.
The winners focus on entity consistency across platforms, genuine community participation over polished marketing, and building interconnected content hubs that demonstrate topical authority. Reddit dominates at 40.1% of all citations, pages ranking below position 20 get cited 89% of the time, and Claude users generate $4.56 per session despite minimal traffic volume.
Most importantly, the measurement crisis means traditional analytics miss the majority of AI consumption, so optimize now based on proxies rather than waiting for perfect attribution data.
We have market clarity reports for more than 100 products — find yours now.
What you should know if you want to appear in LLM answers
1. LLM referral traffic exploded 527% in five months, not gradually
Reality:
Analysis of 400+ websites showed AI-referred sessions jumped from 17,076 to 107,100 between January and May 2025. Legal sites saw 823% growth while Finance hit 612%. This wasn't gradual adoption but a sudden inflection point concentrating 55% of all LLM traffic in just five industries.Logical Explanation:
People started asking AI the kind of questions they'd normally ask experts, not the keyword searches they'd type into Google. AI search hit a tipping point and suddenly everyone was using it way faster than anyone expected.Leverage:
This is a first-mover window closing fast. The growth happened exponentially, not steadily like people predicted, which means early optimizers are riding this curve up while everyone else will face way more competition later. Start now before your competitors figure this out.2. ChatGPT now drives 10% of new signups for technical companies
Reality:
Vercel publicly shared that ChatGPT went from 1% to 4.8% to 10% of new signups across consecutive reporting periods. This caught even the optimistic people off guard.Logical Explanation:
This isn't linear growth, it's exponential. As ChatGPT added citations and more quality content got indexed, the tool became more useful, which drove more usage, which created more citation opportunities. It's a feedback loop.Leverage:
Companies optimizing now are riding this exponential curve upward. The data shows AI search isn't some future thing, it's driving revenue today. If you're in technical products, prioritize ChatGPT visibility right now since getting in early compounds over time.3. Claude sends minimal traffic but generates $4.56 per session
Reality:
Traffic analysis showed Claude accounts for less than 0.001% of AI referrals but produces the highest per-session value at $4.56, followed by Perplexity at $3.12 and ChatGPT at $2.34.Logical Explanation:
Claude users are often developers and researchers who pay for premium AI. They show up with way better purchase intent and dig deeper into content than average users. Different LLMs attract different audiences based on how they position themselves and what they charge.Leverage:
Go after Claude citations now while competition is still low. You'll capture way better returns from a small but high-value audience. Most creators chase traffic volume and completely miss this opportunity. Quality beats quantity here.4. Reddit dominates LLM citations at 40.1% share
Reality:
Semrush analyzed 150,000 citations and found Reddit owns 40.1% of all AI references. That's way more than Wikipedia (26.3%), YouTube (23.5%), or even Google itself (23.3%). This happened because Google paid $60M to license Reddit data and OpenAI integrated it directly into their training.Logical Explanation:
LLMs trust "collective wisdom" over polished marketing. Users want authentic experiences from people who actually use products, not sanitized corporate content. When someone researches a car, they want to hear from the person who drives it daily and admits the radio broke 11 times, not from a paid blogger.Leverage:
Actually participate in Reddit discussions. Be transparent and helpful, not promotional. Share real experiences including the flaws. The same approach we use when compiling our market clarity reports by scraping forums for authentic pain points instead of relying on polished marketing claims. Authenticity beats polish in training data.5. Each LLM platform shows wildly different source preferences
Reality:
Looking at 8,000+ citations shows massive differences. ChatGPT cites Wikipedia 47.9% of the time and basically ignores Reddit. Perplexity pulls 21% from Reddit and 16.1% from YouTube. Gemini loves LinkedIn posts and pulls 4% from community content.Logical Explanation:
Each platform optimizes for different goals and trains differently. ChatGPT wants encyclopedic coverage, Perplexity values recent community discussions, and Gemini integrates professional networks.Leverage:
Treating AI SEO as one channel leaves huge gaps. For ChatGPT, build out Wikipedia presence and encyclopedic blog content. For Perplexity, engage on Reddit and make YouTube explainers. For Gemini, publish thought leadership on LinkedIn. Platform-specific optimization beats generic tactics every time.6. Perplexity uses hidden three-layer reranking system
Reality:
SEO researchers figured out Perplexity has this sophisticated three-step process: initial retrieval, quality reranking, then a final pass that can throw out entire result sets if they don't meet quality standards. That's why some queries say "I don't have enough information" even though millions of relevant pages exist.Logical Explanation:
The system prioritizes fresh content in top categories like AI, technology, science, and business analytics. It heavily penalizes outdated or contradictory info. Quality gates make sure only high-confidence answers get through.Leverage:
One consultant tripled their Perplexity citations by linking to authoritative sources like GitHub repos and Reddit discussions in their developer articles. Show quality through external evidence, not just internal comprehensiveness. Update frequently and align with priority categories. Link out to credible sources instead of just writing comprehensive content.7. Rankings below position 20 get cited 89% of the time
Reality:
Research shows 89% of ChatGPT citations come from pages ranking beyond position 20 in traditional search. 80% of all AI-cited sources don't even appear in Google's top results.Logical Explanation:
LLMs access way bigger content pools, understand semantic relevance beyond keywords, and pull specific chunks that answer user intent instead of just looking at overall page authority. They're not stuck on page one of Google.Leverage:
Stop obsessing over ranking number one for head terms. Create deep, specific content for long-tail queries where you can give the definitive answer even if you're not dominating traditional rankings. Semrush found AI systems increasingly favor content tailored to highly specific use cases over broadly authoritative pages.8. Client-side rendered React apps invisible to LLM crawlers
Reality:
A developer noticed her polished React site never showed up in ChatGPT while her older rough projects appeared easily. Turns out Googlebot executes JavaScript to render SPAs, but AI crawlers like GPTBot and ClaudeBot just read static HTML. Modern AI code generators (Lovable, Bolt, V0, Replit) default to CSR, accidentally making sites invisible.Logical Explanation:
Her React SPA only showed AI bots a JavaScript shell with no text, metadata, or indexable content. The irony is that sites built with cutting-edge AI tools are invisible to AI search because the crawlers don't run JavaScript.Leverage:
Use server-side rendering (SSR) or static site generation (SSG) with Next.js, Nuxt, or similar frameworks. This is a massive hidden advantage for developers still using traditional server-rendered setups or who prioritize SSR. If you built with CSR, you're completely invisible to LLMs right now.9. OpenAI crawls 1,500 pages for every single click sent
Reality:
Cloudflare data shows OpenAI requests 1,500 pages per referred click compared to Google's 18 to 1 ratio (which was 2 to 1 a decade ago). Claude hits an insane 70,900 to 1, making nearly 71,000 HTML requests per single referral.Logical Explanation:
AI systems do massive research behind the scenes before generating answers, eating up tons of content without sending proportional traffic back. 60% of Google searches now end with zero clicks since LLMs just serve the answer. Most people don't bother following the footnotes anymore.Leverage:
Being cited in the actual answer matters infinitely more than being listed as a source. Most users never click through to verify or learn more. Focus on becoming the primary source LLMs use to build answers, not just getting mentioned in references. The business model shifted from traffic to visibility.
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.
10. LLM citations don't generate corresponding server hits
Reality:
A publisher running Vercel/Next.js middleware saw browser requests log normally but tons of LLM citations never showed up in server logs even though they were displayed to users. Plus Claude's native app doesn't include Referer headers, making attribution impossible.Logical Explanation:
LLMs probably use pre-crawled indexes, cached embeddings, or just display citations without fetching in real-time. This breaks the basic measurement where citation equals visit that worked in traditional search.Leverage:
Track AI traffic through referral analytics from chat.openai.com, perplexity.ai, etc., but know this massively undercounts true consumption. You can't accurately calculate ROI on AI optimization with current tools. Smart publishers are demanding transparent telemetry in licensing deals and using proxy metrics like brand search increases and engagement quality instead of direct attribution.11. Users convert 6x better to signups but 5x worse to paid
Reality:
Sentry found a weird pattern where LLM referrals convert to signups at 6x the rate of Google Search traffic (sounds amazing), but those same users convert to paid customers at only one fifth the rate (not so amazing).Logical Explanation:
LLM users probably show up as explorers with high curiosity but lower immediate buying intent, or they're in long research phases. Or maybe AI answers give them enough info to solve immediate problems without needing paid features. Surveys show AI chat ranks number one in "how did you hear about us?" despite only being 10% of traffic, which reveals a massive attribution gap.Leverage:
Optimize LLM visibility for brand awareness and top-of-funnel, not quick conversions. Track long-term attribution instead of just last-click metrics. Build content that gets people in the door with free value, then nurture them over time rather than expecting instant purchases. Just like how our market clarity reports help entrepreneurs understand their markets before they build, which leads to better long-term decisions even if it's not an instant sale.12. Google's AI Overviews traffic shows as organic search
Reality:
Google's AI Overviews and AI Mode run on Gemini but show up as regular organic search in your analytics. When users click citations in AI summaries, the referrer says google.com, not gemini.google.com. Pew Research found 58% of Google users saw AI summaries in March 2025, but all this volume is invisible in "AI traffic" reports.Logical Explanation:
Every report saying "AI traffic is less than 1%" misses the elephant in the room since Google deliberately routes this through their main domain. Real AI search impact could be 10 to 50 times bigger than what's getting reported.Leverage:
Track both explicit AI referrals AND changes in Google organic CTR patterns. If your CTR from traditional results is dropping, it's probably AI Overview cannibalization, not ranking losses. This measurement blind spot is an advantage for early movers who get what's really happening while competitors optimize based on incomplete data.13. ChatGPT users view 42% more pages per session
Reality:
Reddit's Head of Thought Leadership shared that users coming from ChatGPT view 42% more pages per session compared to Google referrals. LLM users already got summarized info and partial answers, so they show up with focused intent to dig deeper, verify details, or actually do something.Logical Explanation:
AI search referrals surged 1,300% during holiday shopping with users engaging way more thoroughly. The higher engagement leads to better conversion rates for lots of sites even with lower absolute traffic volume.Leverage:
Go for fewer but better visitors instead of maximizing clicks. LLM traffic is quality over quantity, which means you need content experiences that reward deep engagement, not quick scanning. Build multi-page journeys and comprehensive resources that match this deeper exploration behavior.14. Only 6-27% of brands optimize for both dimensions
Reality:
Semrush found brands either optimize for "mentions" (showing up in brand comparisons) or "citations" (being the source for facts), but rarely both. Only 6-27% of top-mentioned brands also rank as top sources depending on industry and platform. Zapier ranks number one as a cited source in digital technology but only number 44 in brand mentions.Logical Explanation:
LLMs handle different query types differently. For "best CRM software" comparisons, they pull sentiment from Reddit and review platforms. For "CRM pricing and features" facts, they want structured content from official sites and authoritative publications.Leverage:
Optimize separately for both by creating comparison content for mentions plus comprehensive reference docs for citations. Most competitors miss half their potential AI visibility by only focusing on one. Build both your brand reputation in community discussions and your authoritative documentation for factual queries.15. Community-generated content outranks official expert sources
Reality:
Semrush found Reddit appears in 176.89% of ChatGPT finance queries (almost twice per prompt on average) even though finance is a heavily regulated Your Money Your Life category where you'd think accuracy matters most. Microsoft's corporate blog gets fewer AI citations than Reddit threads talking about Microsoft products.Logical Explanation:
Community content beats experts because LLMs trust collective wisdom over polished marketing and see community discussions as unbiased, factual info they can confidently use. This completely contradicts traditional SEO's focus on E-E-A-T from official sources.Leverage:
Stop making sanitized corporate content. Actually participate in community discussions, share authentic experiences including the bad parts, and trust that collective validation beats official credentials for AI citations. Authenticity wins over polish. When we build our market clarity reports, we prioritize finding these authentic community signals over official marketing claims for exactly this reason.16. Creating deep unsummarizable content protects against cannibalization
Reality:
Google's AI Overviews caused 34.5% CTR drops (position number one fell from 5.6% to 2.6% CTR). Indie hackers pivoted strategy. Instead of fighting AI with robots.txt blocks, successful creators go after "unsummarizable queries" where complex, nuanced questions need strategic depth AI can't compress.Logical Explanation:
One IndieHackers discussion said the playbook changed to targeting queries, building content that's too deep or strategic for AI to flatten, and being deliberate about what not to give away. Factual listicles get easily summarized by AI, making clicks optional.Leverage:
Don't optimize to show up in AI answers. Instead, make content so valuable AI summaries feel insufficient, which drives clicks for full context. Focus on analysis needing human judgment, multi-dimensional trade-offs, or strategic frameworks instead of simple facts AI can package completely.17. Vendor blogs get cited 7% when including competitors
Reality:
Looking at 8,000 AI citations showed vendor blogs get roughly 7% of citations in Perplexity, Gemini, and AI Overviews (only roughly 1% in ChatGPT) when they make comprehensive, genuinely helpful comparison content. Companies like Thinkific, LearnWorlds, Monday.com, and Pipedrive got cited by creating "best X" or "top Y" comparison posts on their own blogs.Logical Explanation:
These companies list themselves first but actually include 10 to 15 competitors with honest pros and cons. This works especially well in niches without much third-party coverage where LLMs don't have authoritative neutral sources. Transparently biased content that's factually comprehensive can beat supposedly neutral sources.Leverage:
Create the comparison content you wish existed, include competitors honestly, and let your unique insights show your superiority. This only works with genuine utility since purely promotional stuff gets ignored. Build trust through comprehensive coverage instead of hiding competition.18. Major companies stopped creating easily-summarized help content
Reality:
After analyzing AI consumption patterns, Sentry killed their "Sentry Answers" technical solutions library (comprehensive how-to content designed for StackOverflow-style discovery). They realized this top-of-funnel content was getting eaten by AIOs without citation, giving value to LLMs while killing their own traffic.Logical Explanation:
With Google's AI mode delivering roughly 5 times fewer clicks, this kind of content only becomes more critical to protect. The logic is to stop feeding AI with content you actually need traffic from.Leverage:
Make either content so deep AI summaries can't replace it, or integrate directly into AI tools as features instead of just searchable content. Sentry doubled down on deep tutorials, solutions pages, MDX documentation for easier LLM digestion, and Model Context Protocol servers enabling direct Claude/Cursor integration. Shift from content-as-traffic to content-as-integration.19. Content needs 91% lists for YMYL, 35% for general
Reality:
A 500-search study analyzing actual Perplexity citations found huge format differences by query type. For YMYL queries (health/finance), 91% of cited articles had lists, only 4% were how-tos, and average length was roughly 1,000 words. For general queries (home/business), only 35% used lists while 30% were how-tos, 15% had "Guide" in titles, and average length hit roughly 1,500 words.Logical Explanation:
YMYL queries needed high-authority domains (universities, government, NerdWallet) while general queries accepted broader sources. Videos auto-loaded in 90% of general queries but only 10% for YMYL. Different query types need different formats for optimal citation probability.Leverage:
Figure out which query type your content addresses, then format specifically for that category instead of using generic structures. The person who implemented these findings saw Perplexity traffic jump 67% with doubled newsletter conversions in two months. Match your format to your query category intentionally.20. Custom GPTs became top-7 traffic source with top-3 conversions
Reality:
Adsby's founder found that Marketing GPTs launched on ChatGPT's marketplace became their 7th biggest organic traffic source since launch while ranking top-3 for conversion rate (free trial signups) among all organic channels, beating blogs and newsletters. The tactic is adding snippets at conversation ends (after users got their result) linking back to the product without messing up the main experience.Logical Explanation:
This is a totally different distribution channel where users find tools while solving problems instead of through content marketing. The conversion quality comes from users who already validated they need this through using the GPT.Leverage:
Build genuinely useful GPTs that give real value, then subtly introduce your paid product as the natural next step. Different GPTs show different usage patterns, giving you insights into which tasks users search for most. Utility first, monetization second.21. Entity consistency prevents LLM suppression more than quality
Reality:
Research found "canonicalization" is critical since LLMs hate contradictions and will suppress citations when they find conflicting entity data. If your headcount shows 2,000 on LinkedIn but 800 on Crunchbase, or your founding year differs across Wikipedia and your website, LLMs might exclude you entirely no matter how good your content is.Logical Explanation:
Citation efficiency ranges from 0.19 to 0.45 across models, and contradictions tank your score. Your structured data needs to be clean before content optimization even matters. This is "entity SEO" separate from content SEO.Leverage:
Make a single source of truth for all brand facts, then keep it consistent across Knowledge Graph, Wikidata, Crunchbase, LinkedIn, G2, and Wikipedia. Use short, declarative, unique sentences for key facts ("Brand employs 2,012 people as of 2025" not "Our company has grown significantly"). Update quarterly and check recall across 20 to 30 query variations. Fix entity data before touching content. The same way our market clarity reports ensure data consistency when analyzing competitive landscapes, so LLMs can trust the information.22. Content hubs with 30+ pieces drove 4,162% growth
Reality:
A case study showed AI SEO rewards comprehensive, interlinked content networks showing topical authority instead of individual optimized pages. Xponent21 built a content hub with 30+ interconnected pieces, an AI SEO glossary defining 50+ terms, extensive schema markup (articles, how-tos, FAQs, author profiles), original research, and case studies with strong internal linking. This drove 4,162% traffic growth in under a year.Logical Explanation:
They hit number one Perplexity ranking and dominated Google AI Snapshots plus ChatGPT citations. Conversion metrics improved with 10% higher engaged sessions, 15% better engagement rates, and 26% faster time-to-information. LLMs reward domain expertise shown through interconnected content depth.Leverage:
Build Wikipedia-style coverage of your domain, not just transactional landing pages. Create comprehensive ecosystems where every piece reinforces your topical authority through strategic internal linking and extensive schema. Exactly the approach we take compiling information in our reports, where interconnecting insights demonstrates comprehensive understanding.23. AI-friendly share buttons help users cite content
Reality:
Indie builders made AI-specific share buttons generating embeddable code that pre-fills prompts for ChatGPT, Claude, Perplexity, and other LLMs with crafted queries including the content. ShareButtons.ai launched with the idea that making it easier for content to get cited and surfaced in tools like ChatGPT, Claude, and Perplexity is the right move, especially as more people use AI as their main research tool.Logical Explanation:
The creator clarified that building brand presence in AI memory means showing up in users' individual conversation histories (their personal context), not the model's training data. After initial interaction, those users might see more brand mentions in future responses.Leverage:
This is user-mediated citation optimization instead of just algorithmic tactics. Help users share via AI the same way traditional social sharing works. Multiple indie hackers showed interest in testing this, recognizing that AI sharing could matter as much as social sharing for content distribution.
Our market clarity reports contain between 100 and 300 insights about your market.
Read more articles
- 30 Content Ideas to Boost AI Visibility
- Ranking in ChatGPT Results: Feedback From 100+ Blog Owners
- Showing Up in AI Overview: 9 Things We've Learned
- How to Get Traffic from ChatGPT: Feedback from 100+ People
