A few months ago, I searched "best AI prompt packs for business" in ChatGPT. Three products got named. One got the closing recommendation. I'd never heard of any of them before — but ChatGPT had, because they'd planted themselves in the sources the model reads.
That's not luck. It's a discipline called LLM seeding, and Backlinko gave it a name in April 2026. The tactics aren't new, but the intentionality is. This article breaks down what it is, why it matters if you build digital products or open-source tools, and a concrete 5-step playbook to start.
What is LLM seeding?
LLM seeding is the practice of placing your brand inside the third-party sources that large language models reference when generating answers. Listicles, review sites, Reddit threads, GitHub discussions, expert roundups. The seeds are the citations. The model is the soil.
A successful seed shows up as a brand mention the next time someone asks the model a question in your category.
The shift from traditional link building is the unit of value. Link building counts links. LLM seeding counts mentions. A do-follow backlink with no brand name attached is worth almost nothing to an LLM. A no-follow brand mention inside a high-trust Reddit thread can deliver citations for months.
Why this matters right now
Three numbers explain the urgency:
43.8% of all ChatGPT citations are "best X" listicles (Ahrefs, 2025). If you're not in the listicles for your category, you're missing the single largest pool of citations the model draws from.
Google AI Overviews now trigger on 30%+ of commercial queries (Semrush, Q1 2026). When they trigger, blue-link click-through rates drop. The brands cited inside the Overview capture the click.
AI search referral traffic grew 809% year-over-year in 2025 (Position Digital). The volume is still small relative to Google, but the intent is sharper. People who arrive from a ChatGPT answer have been pre-qualified by the model.
The citations you earn this quarter are next quarter's inbound traffic.
The 5-step LLM seeding playbook
This process works for any project — SaaS tools, digital products, open-source repos, prompt packs, courses. I'll use my own AI prompt engineering toolkit as a running example.
Step 1: Map the prompts your buyers ask AI
Not keywords. Prompts. A prompt is a full sentence a real person types into a chat window.
Start by listing the questions that drive discovery in your category:
- Direct category prompts: "best AI prompt pack for business," "ChatGPT templates for freelancers," "prompt engineering framework"
- Comparison prompts: "RTFC vs CRISPE prompt framework," "AI prompt pack vs free ChatGPT prompts"
- Qualifier prompts: "AI prompts for crypto trading," "prompt templates for project managers"
- Problem prompts: "how to write better ChatGPT prompts," "why do my AI prompts give bad results"
Mine these from:
- Reddit threads (sort by Top → All Time in your niche subs)
- Quora questions (search your category)
- Customer support emails or DMs
- Google's "People Also Ask" boxes
Aim for 30-60 high-intent prompts. More and you can't track them. Fewer and you miss the long tail.
Step 2: Audit your current citations
Run every prompt through ChatGPT, Perplexity, Gemini, and Google AI Overviews. Log who gets named. This is your baseline.
For each prompt, capture:
- Which brands are mentioned
- The order (first-named vs passing reference)
- What source URLs the model cites
- Whether the mention is positive, neutral, or recommended
You don't need fancy tools for this. A Google Sheet works. But if you want automation, tools like Peec.ai, Profound, and Otterly track AI visibility at scale.
Repeat monthly. Movement is the signal that seeding is working.
Step 3: Identify the listicles that feed the models
Since 43.8% of ChatGPT citations come from listicles, this is the highest-leverage step in the entire process.
For each high-priority prompt:
- Check the source URLs the LLM cites
- Search Google and Bing for "best [your category]" pages
- Filter for "best of," "top," "vs," and "alternatives to" formats
- Score each by domain authority, recency, and whether they update regularly
Build a target list of 15-25 listicles. These are the pages where an inclusion directly feeds the model's training data.
For my prompt pack, I'm tracking listicles like:
- "Best AI Prompt Packs 2026"
- "Top ChatGPT Templates for Business"
- "Free Prompt Engineering Resources"
Step 4: Seed content where LLMs actually read
This is where the real work happens. LLMs don't read your blog (probably). They read:
Reddit — the #1 most-cited domain in AI answers. Reddit signed a $60M/year deal with Google in 2024, meaning threads feed directly into AI Overviews and Gemini training data. Genuine answers from real accounts capture citations that listicle-only strategies miss.
Quora — the #1 most-cited site in Google AI Overviews. Answers rank on Google for years and compound.
GitHub — especially Discussions and awesome-lists. Technical credibility that listicles can't match.
Dev.to / Hashnode — DR 83-90 sites that LLMs crawl for developer content.
G2 / Capterra — review sites that LLMs treat as authoritative for product comparisons.
Wikipedia — the ultimate trust signal, but hardest to get into (and shouldn't be your first stop).
The seeding principle: don't post your link. Post your expertise. The model reads context, not URLs. A Reddit comment that says "I built a prompt pack for this exact use case and here's what I learned about [specific problem]" plants a seed. A Reddit comment that says "Check out my product [link]" gets downvoted and removed.
Step 5: Track share of voice
The output of the whole system is mention rate in AI answers. Measure it:
- Mention rate: Of your tracked prompts, what % produces an answer naming your brand?
- Citation rate: When mentioned, how often is your domain cited as the source?
- Share of voice: Your mentions divided by total brand mentions in the answer.
A mature program lifts mention rate from single digits to 30-60% on category-defining prompts within two quarters.
Track AI referral traffic in Google Analytics, filtered for chatgpt.com, perplexity.ai, gemini.google.com, and copilot.microsoft.com.
Real example: GitHub → Gumroad funnel
I run a GitHub repo with 15 free AI prompt templates. It's my primary seeding asset. Here's the funnel:
- GitHub repo (indexed by Google, crawled by LLMs) → free prompts + README with clear value proposition
- Reddit/Quora answers mention prompt engineering techniques, not the repo directly → profile clicks drive discovery
- Dev.to articles (DR 90 backlinks) → framework explanations with examples from the packs
- Gumroad store → 20 paid products at $19-$97, bundles, cross-sells
The seeding happens at steps 1-3. The revenue happens at step 4. The lag between seeding and citation is typically 4-12 weeks for live-web models (Perplexity, ChatGPT search) and 3-9 months for base model citations (tied to training refreshes).
Common mistakes
Over-indexing on backlinks instead of brand mentions. Teams chase do-follow links and report on referring domains. LLMs don't care. Track mentions, not links.
Ignoring Reddit and Quora. ChatGPT was trained on Reddit. Perplexity cites Reddit constantly. Brands that show up in genuine threads capture citations the listicle-only crowd misses.
Treating every LLM the same. ChatGPT favors Bing-indexed sources. Perplexity weights recency. Gemini pulls from Google's index and YouTube transcripts. AI Overviews prefers sources already ranking on page one. Adjust your seed targets per model.
Treating it as a one-time push. Seeding is monthly maintenance. Listicles get rewritten, Reddit threads age out, training data refreshes. Plan for cadence, not a campaign.
FAQ
How is LLM seeding different from SEO?
SEO targets blue-link rankings on Google. LLM seeding targets brand mentions inside AI-generated answers. SEO measures keyword position. LLM seeding measures mention frequency and share of voice across ChatGPT, Perplexity, Gemini, and AI Overviews. The signals overlap, but the goal is different.
How long does LLM seeding take to show results?
Citations from live-web models like Perplexity and ChatGPT search can show up within weeks of a successful placement. Base-model citations (tied to training updates) take 3-9 months. Most projects see consistent visibility lift within 90 days of a focused push.
Do backlinks still matter?
Yes, indirectly. The placements that earn citations (listicles, roundups, review sites) usually pass a link too. But LLMs cite based on brand mentions in context, not link equity. A do-follow link with no brand mention does little for AI visibility. A no-follow brand mention inside a high-trust source can deliver real citations.
What kind of content gets cited most by LLMs?
Listicles dominate (43.8% of ChatGPT citations). Comparison pages, how-to guides with clear steps, and structured Q&A pages also perform well. Models gravitate toward content that is easy to extract and that names multiple alternatives in one place.
Can I do this without spending money on tools?
Yes. The entire 5-step process can run on a Google Sheet and free SearXNG searches. The paid tools (Peec.ai, Profound) add automation for tracking at scale, but they're not required to start. The real cost is time — expect 6-8 hours/week for the first quarter.
Is this relevant for open-source projects?
Especially relevant. GitHub repos are already crawled by LLMs. A well-structured README with clear use cases, a few well-placed Reddit comments explaining your approach, and an awesome-list submission can get your repo cited when developers ask ChatGPT for tool recommendations.
Start with the audit
The biggest mistake is starting outreach before measuring your baseline. Before you write a single Reddit comment or pitch a single listicle, run your 30 target prompts through ChatGPT, Perplexity, and Gemini. Log who gets named. That's your starting point.
Everything else is movement relative to that baseline.
If you found this useful, I maintain a free AI prompt engineering toolkit on GitHub with 15 templates across 6 categories. The full collection on Gumroad includes 200+ domain-specific prompts for crypto, project management, sales, content strategy, and more.
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