Building a GEO Strategy for Podcasts & Publications

Summary

To increase the likelihood that your podcasts or publications are featured in generative AI answers, they must be technically structured and demonstrate clear authority. This guide give an overview of a Generative Engine Optimization (GEO) framework, covering the necessary steps, from creating machine-readable transcripts and articles to applying specific schema and strengthening credibility signals, to maintain visibility in a new era of content discovery.

Overview

Generative AI has changed how content is discovered. It’s no longer enough for a podcast episode or feature article to rank on Google; if engines like Gemini or ChatGPT can’t understand the structure of your articles, podcast details, or respective listings, that content is less likely to be referenced or cited.

For creators, being visible online now depends on technical clarity as much as editorial quality. This was reflected in a 2025 survey, in which 9 out of 10 practitioners said leadership had asked about AI search visibility in the last 12 months.

Note: the article from Search Engine Land states that AI search drives barely 5% of revenue, according to SEO practitioners. We believe this is because SEOs are approaching generative engine optimization with the same old playbook rather than adapting proven techniques for generative engines.

That pressure reflects how quickly generative platforms are becoming a primary discovery channel. GEO improves the chances that your podcast or publication are cited and referenced.

By combining transcripts, semantic markup, EEAT signals, and structured archives, GEO extends technical SEO into a framework designed for AI-driven discovery. In this overview, we’ll walk through applying GEO principles so your episodes and articles can be mentioned in generative answers.

While the following principles are framed for generative engines, they are not just technical exercises; they are fundamentally about creating a better user experience. A transcript makes your podcast accessible to all audiences, time-stamped notes help listeners navigate to key moments, and a clear byline builds trust with your readers. By optimizing for clarity and authority, you create a higher-quality product for people, which in turn sends all the right signals to AI.

How do I make my Content Machine-Readable?

To a listener, a podcast episode is an hour of conversation. To a generative engine, it’s just audio data. This means you need to include a transcript so each episode’s content is readable. For articles, that means including a clear semantic structure and schema classification. While answer engines can make sense of plain text, they prefer a clear structure.

Let’s use an example of a conservation authority podcast. The basics include:

• Transcripts: A full transcript turns speech into text that engines can parse. If a guest says “sea-level rise,” that phrase becomes searchable and can appear in responses about flooding. Without it, the point is invisible.
• Structured show notes: Time-stamped notes like “12:34 – Dr. Ramirez on urban flooding” help both people and engines locate segments quickly. A single-line summary doesn’t offer the same utility.
• Descriptive titles: “Episode 12” carries no meaning outside your feed. “Episode 12: The Future of Urban Transport” is a context that an engine can recognise and reuse.

For publications, the same principle applies.

• Author name and bio: Connecting the content to a specific author, whose credentials can be cross-referenced, helps engines verify expertise and properly attribute the work.
• Bylines and dates: When these aren’t in the same place every time, engines can’t tag the article properly.
• Headings: An H2, such as “Impact on cities”, helps engines spot and reuse a section.
• References: A link to a report or government dataset gives engines a source to check against.

Without those basics (transcripts, notes, headings, and references), engines will prefer content that offers stronger EEAT signals.

Apply Schema That Matches your Content’s Format

Making content readable is only half the job. It also needs markup. Schema.org vocabulary tells engines the type of page they’re looking at, e.g. a PodcastEpisode, NewsArticle, or FAQPage.

PodcastEpisode schema

Defines the specifics of an episode. Properties such as name, description, duration, and actor (for guest names) make details explicit. That way, if someone asks “Which podcasts has Dr. Ramirez appeared on?”, the episode can be referenced.

Article schema

Highlights the byline, publication date, and section. Marking an article as NewsArticle rather than leaving it generic signals that it’s timely reporting, which increases the chance of being reused in answers about current events.

FAQ schema

Q&A content hidden in transcripts or sidebars becomes machine-readable when marked up with the Question and Answer properties. A health feature with an FAQ on dietary issues can then be lifted directly into a answer engine’s reply.

Note: Use FAQPage only on pages that are actual FAQs with publisher-provided questions and answers; don’t apply to general transcripts or sidebars unless it is reflective of the interview or article format.

Review schema

Star ratings, author info, and review bodies can be tagged with Review and Rating. This gives engines trusted data points they can reuse in comparisons across cultural or product coverage.

Build Structured Archives

Engines don’t just look at one episode or one article in isolation. They scan for patterns that show your work is part of something bigger.

Archive type	Why it matters	Example in practice
Episode libraries	Collected episodes show sequence and context.	A health policy podcast tags its episodes with categories. When someone searches for “mental health”, an answer engine can point to that set.
Article series	Linked articles look like a body of work.	A five-part series on renewable energy ties each piece to the next, so engines reuse the run rather than one page.
Category hubs	Topic hubs pull related content under one roof	A politics section with interviews, explainers, and commentary in one place shows engines they belong together.
Archived metadata	Older pages stay useful when dates and authors are still visible.	An early AI research article that keeps its byline and tags is easier for engines to treat as reliable.

Send Strong Signals

Even with transcripts and schema in place, engines still have to decide whether a source is trustworthy, and authority signals help them make that call. They show that a podcast or publication comes from people and organisations with recognized expertise.

• Author bios: A byline that only shows a name is thin. Adding credentials such as “Marcus Hill, Lead Cybersecurity Analyst at a Fortune 100 bank” gives engines concrete information they can cross-check.
  • Rich bios also help keep older content visible because the expertise remains tied to the author.
• Guest information: Too many podcasts only tag “with John Smith.” That leaves engines with no context.
  • Marking the guest as “John Smith, Director of Operations at a Major Logistics Firm” makes the episode useful in any answer that needs an authoritative view on policy.
• Citations and references: Linking out to well-known data sources (FDA guidance, World Bank reports, or peer-reviewed studies) shows that the content is grounded. Generative engines look for this kind of external verification before reusing material.
• Internal links: Engines also check how a site connects its own work. An article on solar energy that links back to a detailed earlier feature on subsidy policy looks stronger than a stand-alone post. Internal links build a network of related content that engines can reuse as a set.
• Recency bias: Many answer engines give added weight to fresh content; maintaining dates and fixing dead links can help retain visibility.

Consider two sports publications covering football transfers. The first runs a short post with no author, no references, and vague claims about “big moves this summer.”

The second credits a named analyst, cites FIFA transfer data, and links back to earlier articles on player valuations. Engines are far more likely to reuse the second, because it sends strong expertise, authority, and trustworthiness signals.

The contrast is simple but important. Answer engines like ChatGPT, Perplexity, and Gemini, tend to exclude content with weak signals. They lean on content that shows clear authorship, solid sourcing, and links that tie into a broader body of work. The more evidence you build in, the greater the chance your material is reused.

How to Start

Stage 1: Foundational Content and Authority. This is the 80/20. Before touching complex schema, focus on the fundamentals of machine readability and E-E-A-T. This means ensuring every new article has proper headings and clear bylines/bios, and every podcast has a full transcript. These actions provide the most significant initial impact.

Stage 2: The Technical Layer. Once the content fundamentals are in place, begin implementing the appropriate Article or PodcastEpisode schema. This step classifies the clean, authoritative content you’ve already produced, making it explicitly understandable to engines.

Stage 3: Strategic Architecture. With individual pieces of content optimized, the focus can shift to the bigger picture of building structured archives and category hubs. This is an ongoing strategic effort that demonstrates topical authority over time, rather than a one-time fix.

Monitor & Adapt

Optimizing podcasts and publications is an ongoing effort. Engines move quickly, and what worked last quarter might already be out of date. The only way to stay visible is to keep an eye on how things are showing up.

• Validate your schema: Tools like Google’s Rich Results Test or the Schema.org validator will flag errors. Even a small mistake — like a wrong property or a missed comma — can block a page from being read properly.
• Check visibility in practice: use Gander’s Prompt Reports to automatically run your target prompts across ChatGPT, Gemini, and Perplexity. In one view, you can see your citations, mentions, and owned sources. If your content isn’t appearing, open the report’s findings to diagnose likely causes (schema gaps, weak EEAT signals, or canonical issues) and prioritize fixes.
• Keep older content alive: Update dates, swap out dead links, and re-run schema checks. Engines spot when a page looks abandoned, and that hurts trust.
• Stay alert to changes: Standards shift. Podcast schema could expand, or interactive transcript formats might suddenly be expected. Subscribe to Gander’s newsletter to keep on top of GEO trends and best practices.

Keeping Your Podcasts & Publications Visible in AI Search

Generative engines won’t reuse content they can’t read or trust. For podcasts and publications, that means transcripts, schema, archives, and EEAT signals are part of your foundation. If you want your work to keep showing up in Gemini, Perplexity, and other AI-driven platforms, GEO is the framework to follow. Keep it structured, keep it credible, and keep it maintained, and you give your content the best chance of being surfaced when it matters most.