As the founder of Love At First Search, I help coaches, consultants, and service businesses show up in search and AI-generated results. The strategies I teach there — entity authority, consistent metadata, third-party citations, distributed web presence — work incredibly well for those expert-led businesses. But when I applied those same strategies to my romance novels as Bailey Seaborn, I had to rethink all the economics… because the scale is completely different.
A consultant who shows up in Gemini is competing for a $500/hr engagement. One strong recommendation can hit their monthly sales goal. A romance author showing up in ChatGPT when a reader asks “what should I read next” is competing for a $4.99 ebook purchase (or even less).
The mechanisms are the same. The economics mean you need volume… and volume takes time to build. You might have to show up in thousands of AI summaries and recommendations in order to see a change in your revenue.
Does that mean it’s impossible to show up in AI search results? Absolutely not. It is, by no means, a quick win … but if you can crack the code for AI discoverability, it can pay off over the long term. (And y’all know that i love a system that evenutally propels itself over a content hamster wheel.)
This post is an honest breakdown of how AI recommendation systems actually work for books, what signals they’re looking for, and what’s realistically in your control. Including the parts that take longer than you want.
Table of Contents
Amazon isn’t the only place readers find books
Romance readers have always found books through word of mouth — a book club vote, a friend’s recommendation, a Reddit thread, a podcast episode, a conversation in a Facebook group (and my personal favorite, a smutty novel snuck into your locker between classes). And Google search has been rampant, with hundreds of thousands of searches a month for specific tropes and archetypes.
And now, an increasing number of readers are using AI tools the same way they’d use a search engine: typing in what they want and trusting the result.
“Recommend me a grumpy-sunshine romance with trans rep and a happily ever after.” “What should I read if I loved Beach Read?” “Best enemies-to-lovers series on Kindle Unlimited right now.”
Those searches are happening in ChatGPT, Gemini, Perplexity, Claude, and increasingly in Google’s AI Overviews — and the results look nothing like what you’d get from Amazon’s search bar. Amazon shows you what’s selling, what’s converting, what’s getting borrowed in KU. AI tools show you what they’ve learned about books from everything they’ve been trained on: reviews, discussions, author websites, podcast transcripts, Reddit threads, and more.
The criteria for recommendations on Amazon vs. AI recommendations are drastically different. You can have a highly optimized Amazon product page and still be invisible to ChatGPT. You can have a mediocre Amazon listing and show up consistently in Perplexity recommendations if your book has been discussed in enough of the right places.
In a perfect world, your book can show up on both.
How AI systems decide which books to recommend
When a reader asks an AI tool for a book recommendation, the system is essentially running a confidence check: do I know enough about this book, from enough sources, to recommend it without getting the details wrong?
That confidence check draws on a few key factors.
Training data
AI systems are trained on enormous amounts of public web content — Goodreads reviews, Amazon product pages, Reddit discussions, author websites, podcast transcripts, review blogs, library catalogues, and more. A book with a rich presence across many of those sources has more training data weight behind it than a book that exists primarily on its Amazon product page. Traditionally published books have a structural advantage here: the PR machine around a trad release automatically generates the kind of dispersed, third-party coverage that ends up in training data. Indie authors have to build that coverage deliberately.
Corroboration
AI systems look for consistency across platforms. When the same title, author name, cover, and description appears across multiple sources — Amazon, Goodreads, Apple Books, a review blog, a podcast transcript — the system treats that as a high-confidence signal. The book is real, the metadata is accurate, the recommendation is safe to make. A book that only appears in one place is harder to recommend confidently.
Source authority
Not all pages carry the same weight. A Reddit discussion carries more authority than a personal blog with no traffic. A Goodreads review page carries more than a Facebook group post. A published podcast transcript carries more than an Instagram caption. Where the information about your book lives matters as much as whether that information exists at all.
In general, AI prioritizes publicly available pages (not private conversations) on established platforms with easy-to-read content (not just audio files or transcripts).
Recency
Some AI tools — particularly Perplexity and Gemini — retrieve content from the web in real time when answering a question, which means recent content can influence their answers relatively quickly. Others, like ChatGPT and Claude, rely more heavily on their training data, which updates on a longer cycle. In my experience, Grok and Perplexity pick up new signals fastest, Google and Gemini are in the middle, and ChatGPT and Claude tend to be the slowest to reflect new information.
Decide What You Want to Get Found For
If you’re asking, “How long until ChatGPT recommends my book?” then I’m going to encourage you to reframe your thinking.
The better question is: “What do I want ChatGPT to recommend me for?”
If your answer is something generic like, “hockey romance,” I’ve got bad news, babe: As an indie author, you’re probably never going to out-rank Rachel Reid, Elle Kennedy, Hannah Grace and Emily Rath. (I just did a quick Gemini search and it left off Sarina Bowen, Eden Finley, and Tal Bauer. Blasphemy!)
My point is: If you’re an indie author in an established category, you’re competing against authors with a decade of backlist, PR infrastructure, and reader volume you don’t have yet. The same logic that governs your Amazon shelf strategy applies here. A crowded also-bought neighborhood is still crowded, no matter which system is doing the recommending.
So instead of crossing your fingers that you’ll suddenly jump the queue … go find the gap.
Disclaimer: I’m about to advise you to use AI, and I want to be clear about my recommendation so please, keep your anti-gen-AI pitchforks in your shed for just a second. I’m not asking you to go prompt AI to write your book for you. I want you to use AI to interrogate it on knowledge gaps it doesn’t have enough sources, and where your book could fill those gaps. This is purely a competitive landscape analysis, not a suggestion to publish generative AI slop.
Okay, here’s the market research activity:
Paste your blurb or synopsis into the chat and ask the prompt directly: What stands out here as distinctive? Which of these elements have less competition than the others? Have you seen searches for something like this where you didn’t have a strong answer to recommend?
When I was drafting Peppermint Bark, No Bite, I searched Google and Reddit for “foster parent romance” and found almost no recommendations. There were plenty of character who had grown up in the foster care system, but very few where the foster parent was one of the love interests.
At first, I’d avoided including that element in my own blurb — it felt like a spoiler of the emotional arc. But a thin search result is a discoverability opportunity, not a spoiler risk. If almost nobody is writing to that phrase, you’re missing an opportunity to be found.
So I updated my KDP listing, adding “foster parent romance” to both the blurb and the keywords.
The exercise isn’t complicated. Examine what your book actually contains — tropes, professions, family structures, settings, conflicts — and ask which of those elements are underserved in the current market. Then decide, deliberately, whether you’re willing to surface that element more visibly: in your blurb, in your keywords, in your categories.
Don’t expect an AI system to eventually stumble into recommending you. Give it a specific, low-competition reason to share.
What doesn’t work (even though it feels like it should)
Most of what indie authors spend their time on for marketing generates almost no AI discoverability signal … which is, honestly, frustrating as hell for you. I completely understand. That’s one of the key reasons I started publishing on this topic — because there’s a huge lack of information on this topic aimed towards novelists.
You might be spending a boatload of time on the following marketing strategies without realizing that they aren’t going to influence your AI discoverability at all. (And that doesn’t mean they’re bad or wrong — they just have different goals.)
- Social media posts are almost entirely invisible to AI systems. AI is trained to downweight promotional content, and most social posts read as promotional regardless of how organic they feel to you. A BookTok video with 50,000 views does not generate a citable signal the way a Reddit thread does.
Those Instagram reels, TikToks, and Threads posts are still valuable because those are the places that your audience might be hanging out, so they can interact with you on those platforms. They’re fantastic places to nurture replationships, but they’re not built for AI to cite. - Amazon ads generate sales signals inside Amazon’s ecosystem but create no external web presence that AI can find or cite.
- Newsletter broadcasts land in your reader’s inboxes but generate no crawlable content. They’re essentially invisible to AI. They’re still a fantastic way to engage with your audience. Swaps and cross-promotions drive traffic and be great ways to grow your subscriber base, but AI can’t see any of that hard work.
- Private Facebook groups and reader communities aren’t indexed by search engines and don’t exist in AI training data.
- ARC reviews that never get posted publicly — if your reader loved the book but only told you in a DM, that signal doesn’t exist for AI purposes.
- Paid promotional newsletters (Freebooksy, BookBub ads) drive downloads but don’t generate citable content about your book.
None of those activities are worthless — some of them are valuable for Amazon discoverability and reader relationships. But if you’re spending most of your marketing time in a social media wind tunnel, it most likely won’t turn into broader AI discoverability.
Is AI more likely to recommend traditionally published books over indie?
The books that AI systems recommend most confidently are, disproportionately, traditionally published books. Not because they’re better (reader satisfaction data consistently shows no quality gap) but because the PR infrastructure around a trad release generates the kind of dispersed, authoritative, third-party coverage that AI training data is built on.
A traditionally published romance gets reviewed in mainstream media, listed on “best of” roundups, catalogued by library systems, and referenced in literary coverage. An indie author has to build every one of those signals from scratch.
This is a real structural disadvantage for indie authors, and it’s not new — PR resources are one of the key reasons that authors often choose to go trad instead of indie. AI has raised the stakes of that coverage gap, because readers are now getting recommendations from systems trained primarily on the content that publisher PR machines generated.
So if your book isn’t showing up in AI search, it’s not your fault. You’re building infrastructure in a system that takes time, against a headwind that isn’t going anywhere. The answer is the same one it’s always been: build the right signals, consistently, over time.
So what exactly can an indie author do to make themselves more discoverable?
The 5 levers that help AI discover your book
1. Build a dedicated, information-rich page for each book on your author website.
Your Amazon product page is built for conversion. Your author website book page should be built for information.
An AI system looking to recommend your book wants to know:
- Title, series name, and where it falls in the series
- Tropes (Amazon metadata rarely includes these explicitly)
- Heat level
- Rep and content notes
- Comparable titles and authors
- Publication date
- A description written in natural reader language (not keyword-stuffed copy)
- Pull quotes from reviews
Each book needs its own page, not a shared catalog page. Separate pages let AI systems build accurate, distinct knowledge about each title. A shared catalog page creates ambiguity.
2. Prioritize Goodreads reviews.
Goodreads is one of the most consistently cited sources across all major AI tools for book recommendations. Reader reviews there are exactly the kind of dispersed, human-curated, third-party content that AI systems are trained to weight highly.
Detailed reviews matter more than short ones. A review that names tropes, describes the emotional experience, and mentions comparable authors is more useful training data than “5 stars, loved it!” Your existing readers may have already loved your books without ever posting on Goodreads — a direct ask to your mailing list or ARC team costs nothing and can move this number meaningfully.
3. Get your books discussed on Reddit.
Reddit is currently the most-cited domain across ChatGPT, Gemini, and Perplexity for book recommendations — more than any other single platform. This is partly because Google and OpenAI both have data agreements with Reddit giving their systems privileged access to Reddit content, and partly because Reddit’s moderation and upvote systems produce the kind of human-validated content AI systems are trained to trust.
You cannot promote your own books directly on most romance subreddits — r/RomanceBooks, r/KindleUnlimited, and similar communities have strict anti-spam rules and will ban you for it (Made that mistake myself by including one of my books in a list of 10 recs without disclosing it). What you can do is ask your existing readers, in your back matter or to your mailing list, to mention your books in threads where they’re naturally relevant. An established Reddit account organically recommending your book carries more AI discoverability weight than almost anything else on this list.
4. Pitch yourself for podcasts and pursue book-specific coverage.
AI systems are trained on public podcast transcripts, which are increasingly common thanks to automatic transcription tools. An episode where you discuss your books — their tropes, comparable titles, the emotional arc, why you wrote them — generates rich, crawlable, contextual content that compounds over time.
The most effective podcast appearances for AI discoverability are ones where multiple facets of your identity appear together. When I pitch myself as a guest, I’m pitching the intersection: an AI discoverability consultant who also writes romance novels and teaches indie authors how Amazon’s algorithm actually works. Every transcript that captures all three of those threads simultaneously is building the web of connections that AI systems use to understand who I am and what I do.
You don’t need a complicated pitch strategy to apply this principle. If you co-host a podcast, use it. My co-host Nikki McKnight and I record a dedicated episode for every Bailey Seaborn book release on First Dates & Soulmates — we go deep on the trope, the plot, what I was thinking when I wrote it, and why readers who loved a certain kind of romance will love this one. That episode exists as a permanent, indexed, transcribed piece of content about that book.
For book-specific coverage beyond podcasts: pursue guest posts on author blogs with established readership, seek out romance-focused blogs that feature author spotlights, and look for any opportunity to get your books mentioned by name in a context that will be publicly indexed.
5. Distribute consistently — even in paperback.
The more places your book appears with consistent metadata, the stronger the corroboration signal. This is complicated for Kindle Unlimited authors because of the exclusivity clause of enrolling in Kindle Select, but even within KU there’s a move worth making.
Even if you’re KU-exclusive for your ebooks and don’t expect meaningful print sales, it’s worth setting up paperback distribution through Draft2Digital or IngramSpark. Your ebook won’t get into into Apple Books or Kobo, but you’ll get listings on sites like Barnes & Noble and Bookshop, links on other physical retail sites, and the ability for indie bookstores to stock your book. Each of those is an additional corroboration point — another place on the web where your title, cover, and author name appear together consistently.
If you are a wide author, make sure your metadata is genuinely consistent across every platform: Same series name formatting, same author name, same cover. Inconsistencies across platforms create exactly the conflicting signals that reduce AI confidence in your books.
This isn’t an overnight tactic, it’s a long game
If you’re still wondering, “How long will this take?” The answer is: It depends.
AI models take time to train and update. A well-optimized web presence today may not influence AI recommendations for six months to a year. Grok and Perplexity pick up new signals fastest; Google and Gemini are in the middle; ChatGPT and Claude tend to be slowest to reflect new information.
But honestly, most things about indie publishing are a long game, aren’t they? Most debut novels don’t find their audience immediately. Most authors report that it takes a substantial backlist — often 5 to 10 books or more — before income becomes consistent and predictable. Building AI discoverability follows the same curve. The authors accumulating signals now will show up reliably in AI recommendations in 1-2 years.
The practical implication: Stop spending all your marketing time in the social media wind tunnel. Pitch yourself for podcast appearances and traditional media opportunities. Encourage your readers to post about you on Goodreads and Reddit rather than (or in addition to) tagging you on Instagram. Build your author website book pages properly and leave them there to accumulate authority. Each of these compounds in a way that social posts don’t.
