Lindsay Randall’s excellent article on this blog about GEO & SEO explains how websites help establish digital authority. But AI-assisted discovery doesn’t rely only on your website. Increasingly, recommendation systems pull signals from retailer metadata, reviews, author profiles, and consistent cross-platform information. The good news? Authors can improve discoverability without gaming the system.
Here are five practical steps.
Step 1: Make your book descriptions emotionally searchable.
We’ve usually focused our book descriptions on human readers, often expecting them to infer things like tone from the setting and cover design. Make these more explicit for AI systems to recognize.
Consider adding more keywords to your title and subtitle. “A dark fantasy romance” and “a fast-paced domestic thriller” not only signal to your readers, but the AI as well.
What are the tropes in your book? Add “single dad” or “enemies to lovers” in your subtitle, where appropriate in genre fiction.
In your description, include comparable reads. “Perfect for fans of John Grisham and Lisa Scottoline” reinforces to systems that your book is a legal thriller, while “For fans of Janet Evanovich and Jana DeLeon” tells the AI your book is fun and romantic.
Step 2: Clean up your metadata everywhere.
Make sure that you use the same series title, in the same word order. Systems are picky! They may not see “A Victorian MM romance” the same as “A Victorian Historical MM romance.”
Find the best set of keywords for your book, and use them at all retailers as well as in your description.
Conflicting metadata weakens machine confidence. Search your online reviews for the keywords and terms readers use in describing your book. Are your reviews hitting the same points as your descriptions? Maybe you think your book is sexy, but reviewers find it sweet.
Don’t call the same book “romantic suspense” on Apple, “thriller” on Amazon, or “mystery” on your website. The clearer your audience targeting, the easier AI can classify your book.
Step 3: Claim your author identity.
Lindsay mentions the importance of “experience, expertise, authoritativeness, and trustworthiness (which is known as E-E-A-T)” on your website. That carries through to every platform where you appear. The more places you appear where your profile is consistent, the less likely automated systems are to question your legitimacy.
Review your Amazon Author Central profile, and see how your Author Page looks for readers in different countries. How does this compare to the bio on your website, or the “about the author” material in your books? Ralph Waldo Emerson may have said, “A foolish consistency is the hobgoblin of little minds,” but consistency is important for AI systems.
You may be tempted to create a more professional persona on a business site like LinkedIn, leaning on your work experience. But it’s important that you find a way to connect your writing there as well. LinkedIn can do double duty, reinforcing your credibility while helping new readers find you.
Step 4: Strengthen your series signals.
Series give recommendation systems stronger signals because reader behavior around series is highly predictable.
Make sure you have a clear path to entry. Is this first volume a reader magnet, or book one? For years, traditional publishers resisted putting series order on book covers because a store might only stock the most recent book, and readers might balk at picking up book seven in a series. But numbering your books helps readers see where they want to begin, and with online retailers your books are always available. It may help readers choose you as well to see that you have written consistently. You can always include a statement like “This is book six but can be read as a standalone” if that’s the case.
Your series branding should be consistent as well. If you write both contemporary romance and hard science fiction, your branding will help the AI distinguish between the two.
Step 5: Help readers describe your books accurately.
You can’t control reviews, but you can influence clarity.
“I loved it!” is nice for the ego but useless for discoverability. Reviews that mention character, overview, plot, and style (my shorthand: COPS) give AI far richer signals about what your book actually is. I often include this framework when asking for reviews because it’s catchy and easy to remember.
And remember, even a bad review can be useful. “I couldn’t keep reading because I was too frightened,” says something distinct about your book—as long as it’s not a comic romance!
AI systems look for publicly available reviews on sites like Goodreads, Reddit, blogs, podcast transcripts, and online book discussion groups. Reddit is a surprisingly strong source, because readers there feel more open about making comments within a specific subgroup.
AI learns from how readers talk about books.
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Neil S. Plakcy is the author of more than 70 novels in mystery, romance, and adventure. A former college professor who trained faculty in the effective use of technology, he now teaches writers about practical, ethical uses of AI for authors.