How to Write Content That Works for Search Engines and AI Answers

Applied LSEO example: make the answer auditable

Firsthand publishing example, checked October 24, 2026: LSEO’s AI share-of-voice guide includes a worked calculation that readers can inspect. It states a 40-answer sample, counts each tracked brand once per answer, lists the brand counts, and shows both denominators.

That produces two different answers to two different questions: 12/40 is a 30% mention rate; 12/36 is a 33.3% share of tracked brand-answer mentions. The numbers are explicitly illustrative. The page also explains why an answer can contain several brands and why a zero denominator leaves share of voice undefined.

The editorial lesson: show the unit being counted, the sample, the arithmetic, and the boundary of the claim. A reader can reproduce this answer without trusting an unexplained score. This is an example of LSEO’s published explanatory structure; it supplies no evidence that the revision improved rankings or AI citations.

Start with the job the user is trying to complete

Traditional SEO often begins with a keyword. AI search makes it more obvious that the keyword is only shorthand for a larger need.

Someone searching “enterprise SEO agency” may be trying to build a shortlist. Someone asking an AI assistant “What should I look for in an enterprise SEO agency?” is exposing more of the decision process. The two queries are related, but the second makes the buyer's real task clearer.

That is why intent should sit above both keyword targeting and prompt targeting. The page should help a user complete a job: understand a concept, compare options, evaluate risk, choose a provider, solve a problem, or take the next step.

The Wix Studio / Peec study of 75,000 AI answers and 1,056,727 citations found that prompt intent was more predictive of the type of content cited than either industry or model. Informational queries leaned heavily toward articles, while commercial queries cited listicles much more often. The implication is practical: choose the content format based on the decision the audience is making.

Use an answer-first structure without turning the page into fragments

A useful page should make the core answer easy to find. That does not mean every paragraph has to become a two-sentence “AI chunk.”

Google's current guidance for generative AI features in Search explicitly says there is no requirement to break content into tiny pieces for AI systems and no need to rewrite content in a special AI-only style. Google also says its generative Search features continue to rely on foundational SEO practices and helpful, unique content.

The better approach is editorial rather than mechanical:

• State the direct answer early when a direct answer exists.
• Use H2s and H3s that map to meaningful subquestions.
• Keep each section focused on one clear part of the topic.
• Use lists and tables when they genuinely improve comprehension.
• Follow concise answers with evidence, nuance, examples, and exceptions.

This gives the reader a fast path to the answer while preserving the depth required to understand why the answer is true.

Build a non-commodity layer into every important page

If a competent model could generate essentially the same page from public summaries, the page has a differentiation problem.

Google's people-first content guidance asks whether content provides original information, research, analysis, first-hand expertise, substantial value, and clear sourcing. Its newer AI-search guidance goes even further by encouraging unique, non-commodity content rather than material that simply restates what is already available elsewhere.

For marketers, that means every high-value page should contain something that is difficult to replace with generic synthesis.

The strongest content often combines both. Data gives the page something original to say. Expert interpretation gives the reader a reason to care.

Make important facts specific enough to retrieve

Generic language creates ambiguity. Specific language creates usable information.

Instead of saying “our platform integrates with leading tools,” name the tools. Instead of saying “we work with large organizations,” define the customer profile. Instead of saying “results improved significantly,” provide the metric, time frame, and methodology if those details can be substantiated.

LinkedIn and Meltwater's analysis of approximately 9.5 million AI citations found recurring structural patterns among highly cited LinkedIn content. In the 24 most-cited URLs they examined more closely, 100% used lists, 92% used clear headings, 75% named companies or tools, and 67% used hard data. The important caveat is that these percentages describe that 24-URL subset, not all 9.5 million citations.

The practical lesson is not to stuff pages with names and numbers. It is to replace vague marketing language with concrete entities, verifiable facts, and useful distinctions.

Write for the initial query and the follow-up questions

Classic search behavior often looks like a sequence of separate searches. Generative search can collapse that sequence into a conversation.

A user may start with “What is GEO?” and immediately continue with “How is it different from SEO?”, “What should we measure?”, “Do we need new schema?”, and “How would I implement this for a B2B company?”

A strong page anticipates the natural next questions without trying to target every imaginable prompt variation. This is where depth becomes useful. The page should cover the decision tree around the topic, not create one near-duplicate page for every long-tail wording.

Google specifically advises against rewriting content just to capture every phrasing of an AI query. Its systems can understand synonyms and broader meaning. So content teams should focus on topical completeness and user progression rather than prompt-by-prompt page proliferation.

A practical writing workflow for SEO and AI answers

What traditional SEO still contributes

AI search has not made basic SEO irrelevant. It has made it easier to see which parts of SEO were always about clarity and accessibility rather than gaming an algorithm.

Do not optimize for AI at the expense of the reader

Some GEO advice has drifted toward writing pages for machines: repetitive question blocks, unnatural definitions, hundreds of prompt variants, excessive schema, or formulaic “answer capsules” on every section.

That is the wrong direction.

Google says there is no special schema.org markup required for its generative Search features, no Google Search benefit from llms.txt, and no requirement to “chunk” content into tiny pieces. The search engine is explicitly telling publishers not to build a second artificial content language for AI.

Other platforms have their own access rules. For example, OpenAI's publisher guidance for ChatGPT search says public sites can appear in ChatGPT search and recommends allowing OAI-SearchBot when publishers want their content discovered, summarized, cited, and linked. That is an access consideration, not a writing formula.

The writing standard remains human: be useful, accurate, specific, original, and easy to navigate.

A content quality checklist for both search and AI

Before publishing, review the page from both a reader's perspective and a retrieval perspective.

• Intent: Does the page solve a specific user problem rather than merely target a phrase?
• Directness: Can the reader find the main answer quickly?
• Depth: Does the page answer the natural follow-up questions without padding?
• Originality: Is there proprietary data, first-hand experience, analysis, methodology, or a distinct point of view?
• Evidence: Are important claims supported by reliable sources or clearly described first-party evidence?
• Specificity: Are important companies, products, people, locations, metrics, and criteria named where useful?
• Structure: Do headings, lists, tables, and sections make the logic easy to follow?
• SEO fundamentals: Are the title, headings, internal links, media, canonicals, and technical access handled correctly?
• Trust: Is it clear who created or reviewed the content and why the reader should trust the conclusions?
• Conversion: Is the next step relevant to the user's stage rather than forced into the page?

The goal is one content system, not two

The least efficient response to AI search is to create a parallel content factory: SEO pages for Google, separate “AI pages” for answer engines, and dozens of near-duplicate articles for prompt variations.

A stronger model is to build one content system around buyer intent and evidence. SEO ensures that the content can be discovered, understood, and ranked. AEO improves the clarity of direct answers. GEO expands the strategy toward the sources, formats, entities, and third-party evidence that can shape generative answers.

Those disciplines should reinforce each other. The page still needs to satisfy the person reading it. That is the common denominator.

Write the best answer you can substantiate. Organize it so the important parts are obvious. Add something original. Name what matters. Keep the technical foundation clean. Then measure how the content performs across both search traffic and AI visibility rather than assuming one channel has replaced the other.