The New Playbook For SEO/GEO Content in 2026

The move from ten blue links to AI-generated answers means the biggest opportunity isn't just ranking, it's becoming the source AI systems trust and cite. In this session Zach shows content teams and marketing leaders how to make their pages "answer-ready" for Generative Engine Optimization, covering how AI evaluates content at the passage level, why chunking and structure matter more than ever, and how semantic relationships and "triples" help AI connect your brand to the topics that matter. He'll also dig into synthetic fan-out queries, the follow-up questions AI generates behind the scenes, so you can build content ecosystems that anticipate what AI asks next. You'll leave with a marketer-friendly framework for writing content that isn't just searchable but usable in AI-driven discovery, no engineering background required.

Transcript

1. The New Playbook for GEO Content in 2026 How to Get Your Brand Chosen as the Answer Zach Chahalis VP of Relevance Engineering & Analytics iPullRank X: @ZachChahalis LI: /zacharychahalis
2. I’M ZACH CHAHALIS VP OF RELEVANCE ENGINEERING & ANALYTICS iPullRank My context: 16+ Years in SEO, Marketing Analytics, and CRO 1 Year at iPR (But 3 yrs in total) 11 Yr Old Goldendoodle and 4 Yr Old Aussiedoodle Six Months Married
3. AGENDA ❏ How AI Search Actually Works ❏ The GEO Selection Funnel (Why Content Wins or Loses) ❏ Turning Insights Into Action (How Marketers Should Structure Content) ❏ Reframing GEO as a Content Strategy (Not a Toolset) ❏ Scaling Visibility With Omnimedia & Semantics ❏ Proof: Case Studies ❏ Wrap-Up/Next Steps The New Playbook For GEO Content in 2026: How To Get Your Brand Chosen As The Answer 3
4. TRADITIONAL SEO ENSURES THAT YOUR SITE IS DISCOVERED, GEO ENSURES THAT YOUR SITE IS UNDERSTOOD 4
5. LET’S TALK ABOUT HOW AI SEARCH ACTUALLY WORKS 5
6. STRATEGIC IMPLICATIONS AI SEARCH REQUIRES MULTI-DIMENSIONAL APPROACHES Generative search is not a single ranking contest for a single query. It is a multi-stage filtering process in which your content competes at dozens of points in a branching, multimodal retrieval plan. The fan-out means the system is looking for breadth as well as depth, and the synthesis step means it is judging your content on extractability and readiness, not just relevance.
7. MATCHING THE ROUTING PROFILE WHERE OPPORTUNITIES ARE WON OR LOST Content must match the expected modality or it won’t be retrieved Ensure multi-modal parity (text, structured data, transcripts, etc.) Place content where the routing logic looks (e.g. API-friendly formats, transcripts for procedural content) Align content with routing profiles to increase retrieval across fan-out branches
8. CROSS-DOMAIN EXAMPLE: FINANCE Routing here would send rate queries to financial data APIs, minimum deposit requirements to bank product pages, insurance explanations to government or educational sources, and comparison logic to personal finance editorial sites. Each of those is a different source type, with a different retrieval method and cost profile.
9. THE GEO SELECTION FUNNEL (WHY CONTENT WINS OR LOSES) 9
10. SELECTION FOR SYNTHESIS Selection is not only about relevance to the sub-query. It is also about the suitability of a chunk to be lifted, recombined, and integrated without introducing factual errors, formatting issues, or incoherence. In effect, the system is ranking not entire pages but atomic units of information, and the scoring criteria are tuned to synthesis needs rather than to click-through behavior.
11. EXTRACTABILITY AS THE FIRST GATE If a chunk cannot be cleanly separated from its surrounding context without losing meaning, it is less valuable to the synthesis process. This is why content that is scoped and labeled clearly tends to survive selection.
12. EVIDENCE DENSITY AND SIGNAL-TO-NOISE RATIO THE SYSTEM IS LOOKING FOR VERIFIABLE INFORMATION Once extractability is established, the system looks at evidence density or the proportion of meaningful, verifiable information to total tokens. A dense paragraph that gives a clear statement, followed by an immediate citation or supporting data, is more valuable than a lengthy, anecdote-heavy section that buries the facts in storytelling.
13. SCOPE CLARITY AND APPLICABILITY Generative systems are sensitive to scope because they are trying to assemble an answer that is not misleading. If a chunk does not make clear the conditions under which it is true, it is harder to place it correctly in the final answer.
14. AUTHORITY AND CORROBORATION The system also weighs the credibility of the source and the degree to which the information is corroborated by other retrieved chunks. Authority in this context is not limited to domain-level trust; it can apply at the author or publisher level. Corroboration is a subtle but important factor. If three independent, credible sources agree on a specific mileage progression, that progression is more likely to survive selection. Outlier claims may still make it in if they are well-sourced, but the system will often prefer information that has multiple points of agreement.
15. FRESHNESS AND STABILITY Recency is another filter, especially for topics where the facts can change. A chunk that is clearly dated and shows evidence of recent review is more attractive to the model than one with no temporal markers.
16. HARM AND SAFETY FILTERS Finally, selection often applies harm and safety filters. These filters can be domain-specific, drawing on both explicit policies and learned patterns from training data.
17. WHY GOOD CONTENT GETS EXCLUDED High-quality content can be excluded from synthesis if it isn’t easily extractable— interactive designs that aren’t crawlable or long-form narratives that bury key facts risk being skipped in favor of denser, more accessible material.
18. TURNING INSIGHT INTO ACTION (HOW MARKETERS SHOULD STRUCTURE CONTENT) 18
19. ENGINEERING FOR THE SELECTION FUNNEL In practice, this means rethinking how you structure your content. A single long page may need to be designed as a series of clearly marked, self-contained modules, each of which could stand alone if lifted into a generative answer. It also means pairing each chunk with whatever metadata, markup, and alternative formats will make it easier for a retrieval system to recognize and use.
20. WRITING FOR SYNTHESIS To ensure your content performs well in modern retrieval systems, it’s essential to structure it in a way that is both machine-readable and human-friendly. Embedding models rely on clean, well-defined “chunks” or semantic units of information to generate precise and relevant results.
21. PASSAGE OPTIMIZATION Optimizing for extractability means that content should be organized into easily defined sections. Headings and subheadings should be clear, and passages should answer queries directly and succinctly. The combination of query/passage is defined as a semantic unit, and these units are used to power AI search.
22. CHUNKING IS ABOUT IMPROVING YOUR USER EXPERIENCE, NOT JUST AN EXERCISE FOR CRAWLERS 22
23. WHEN WE SAY “CHUNKING” WHAT WE REALLY MEAN IS IMPROVING CONTENT STRUCTURE AND DESIGN Chunking and writing for humans are not mutually exclusive. Cyrus Shepard talks about how better structure yields better performance across a variety of metrics. Combining similar UX principles with Content Engineering gives you a feedback loop to improved performance. Source: https://moz.com/blog/10-super-easy-seo -copywriting-tips-for-link-building
24. HERE’S AN ORIGINAL PARAGRAPH TARGETING [MACHINE LEARNING] AND [DATA PRIVACY] The development of sophisticated algorithms capable of learning from vast datasets has revolutionized numerous industries, enabling predictive models that can identify patterns and make decisions with minimal human intervention; this process of training models on historical information is the core of modern artificial intelligence. However, the collection and use of this data, especially personal information, raise significant concerns about individual rights and the potential for misuse, leading to the establishment of regulations like GDPR and CCPA. These legal frameworks mandate that organizations implement robust security measures, such as encryption and anonymization, to protect sensitive information from unauthorized access. The challenge lies in balancing the insatiable need for high-quality training data, which improves model accuracy and performance, with the ethical obligation to ensure that an individual's personal details are not compromised, requiring techniques that can safeguard information while still allowing for valuable analytical insights to be drawn. Cosine Similarity Score Machine Learning 0.6481 Data Privacy 0.6948
25. NOW I SPLIT IT INTO TWO PARAGRAPHS TARGETING [MACHINE LEARNING] AND [DATA PRIVACY] Cosine Similarity Score Chunk 1 (vs. "Machine Learning") 0.7477 Chunk 2 (vs. "Data Privacy") 0.7634
26. REFRAMING GEO AS A CONTENT STRATEGY, NOT A TOOLSET 26
27. OUR STRATEGIC APPROACH FOR DRIVING VISIBILITY IN AI SEARCH
28. TACTICAL IMPLEMENTATION 5 STEPS TO UNDERSTANDING THE GAPS IN CONTENT Relevance Engineering is the process of adjusting content so it’s selected and cited by Google’s AI systems like AI Overviews and AI Mode. Unlike traditional SEO, which focuses on ranking full pages, this approach targets the individual passages and concepts that AI uses to construct responses
29. EXPAND KEYWORDS WITH QFORIA THIS HELPS YOU UNDERSTAND YOUR CONTENT GAPS Qforia extrapolates synthetic queries based on the initial prompt and gives you their type and reasoning similar to what Google is doing. https://ipullrank.com/tools/qforia 29
30. QFORIA HAS BEEN QUIETLY UPDATED IT DETERMINES THE EXPECTED TYPES OF CONTENT PER TERM The concept of routing content is now used as part of the pipeline so it tells you the type of content Gemini would consider the best match for that subquery. 30
31. NOW LET’S SCALE VISIBILITY WITH OMNIMEDIA CONTENT AND SEMANTIC OPTIMIZATION 31
32. OMNIMEDIA CONTENT STRATEGIES It requires us to think beyond text and align with the expected content formats and locations to drive visibility.
33. UGC For queries involving troubleshooting, product comparisons, lived experiences, or niche use cases, user-generated content (UGC) and forum discussions are often prioritized by AI systems. Generative models value this type of content because it reflects authentic, diverse, and situational insights that can’t always be found in more polished corporate content.
34. ENTITY-RICH, EMBEDDING-FRIENDL Y LANGUAGE Write with clearly defined entities Use consistent terminology Include modifiers and descriptors: Qualifiers like size, function, location, and purpose help differentiate similar entities.
35. STRUCTURED DATA Know that LLMs can and do use structured data as part of RAG pipelines. Use the entire Schema.org vocabulary, not just the ones that drive rich results. Structured data improves comprehension of your content and entities.
36. SEMANTIC TRIPLES Semantic triples help search engines understand context better by identifying entities, establishing connections, and building a web of interconnected concepts, which provide richer contextual information beyond just keywords. These Subject–Predicate–Object triples are the building blocks of knowledge graphs, which allow AI systems to understand relationships between entities, enabling more intelligent search results, factual verification, and structured data for AI overviews.
37. USE SEMANTIC TRIPLES Semantic triples (subject-predicate-object) significantly boost retrieval accuracy and content relevance. To do this, always use the active voice.
38. PROVIDE UNIQUE, HIGHLY SPECIFIC, OR EXCLUSIVE INSIGHTS Unique content or proprietary data increases the likelihood that your page is retrieved and cited as authoritative in RAG pipelines.
39. AVOID AMBIGUITY Clearly defined, straightforward sentences reduce embedding noise and retrieval errors.
40. PROOF IS IN THE CASE STUDIES 40
41. 2X GROWTH IN AI SEARCH VISIBILITY FOR A VEHICLE SALES PLATFORM It’s still early days, but we’re seeing great results for our clients on AI Search platforms. 661% GROWTH IN CHATGPT, 330% GROWTH IN AI OVERVIEWS Our client in the automotive industry has experienced substantial growth across all major generative AI search surfaces, including Google AI Overviews, ChatGPT, and Perplexity. This growth has been driven by a combination of technical improvements and strategic content enhancements. Notably, they’ve achieved significant visibility gains on non-branded queries that previously eluded them. THESE ARE THE RESULT OF RELEVANCE ENGINEERING EFFORTS To drive these results, the client prioritized enhancements to their JavaScript rendering to ensure full crawlability and indexation. They also expanded their content footprint with new, high-intent pages and leveraged detailed competitive analyses to identify and close semantic gaps. These efforts were supported by a structured content engineering process that aligned page copy with user intent and topical authority.
42. HOW A CATEGORY LEADING TELECOMMUNICATIONS BRAND ACHIEVED 253% GROWTH IN AI OVERVIEWS VISIBILITY THE RESULTS ❏ 1.41 million impressions in AI Overviews ❏ AI Overview inclusions grew from 712 to 3,235 in one year ❏ Steady month-over-month growth in AI-driven visibility THE BACKGROUND A major telecommunications provider wanted to expand its footprint in AI Search. Traditional SEO had secured rankings in organic results, but their visibility in AI Overviews was lagging, especially for competitive, branded queries where consumers increasingly rely on generative answers. The brand recognized the need for a strategy tailored to the mechanics of AI search. WHAT WE DID We applied a content engineering optimization framework, including: • • • Restructuring content with semantic clarity and answer-like formatting to make it extractable for AI Overviews. Enhancing technical accessibility with clean HTML hierarchy, sitemaps, and schema markup to support generative retrieval. Creating entity-rich, data-backed passages aligned with how AI models build summaries. This combination of technical SEO and AI-native content structuring enabled the client’s pages to be consistently selected for AI Overviews, compounding visibility over time. GENERATIVE AI CASE STUDY OUR GOALS ❏ Expand visibility in AI Overviews for branded and competitive queries ❏ Increase generative search presence and organic impressions ❏ Position telecom content as authoritative, extractable, and AI-ready SERVICES USED ❏ Competitive Analysis ❏ Content Engineering ❏ Content Audit ❏ Content Plan
43. WRAP UP AND NEXT STEPS 43
44. CONTENT ASSESSMENT FLOW CHART TITLE
45. SCREAMING FROG + OLLAMA I generate embeddings as I crawl, asset content, take screenshots and analyze as I crawl. All on my own local GPU. The New Playbook For GEO Content in 2026: How To Get Your Brand Chosen As The Answer
46. 24 CHAPTERS OF PURE 🔥🔥🔥 Everything you need to know about how AI Search works. No vagueries. The New Playbook For GEO Content in 2026: How To Get Your Brand Chosen As The Answer
47. ipullrank.com/ai-search-manual AI Search to Sale: What the Data Reveals About AI Search eCommerce Behavior The New Playbook For GEO Content in 2026: How To Get Your Brand Chosen As The Answer
48. THANK YOU Zach Chahalis VP of Relevance Engineering & Analytics iPullRank X: @ZachChahalis LI: /zacharychahalis Title Tap in with us: ipullrank.com The New Playbook For GEO Content in 2026: How To Get Your Brand Chosen As The Answer
49. THANK YOU