How To Create Content Based on SERP and LLM trends (Agencies, Consultants, Startups)

Want to create content based on SERP and LLM trends that rank on Google and appear in answers from ChatGPT, Claude, or Gemini? As search evolves across both engines and AI models, the rules of visibility are shifting fast.

Many still view this only through search engine optimization (SEO), but visibility now means showing up in both Google’s search engine results pages (SERP) and large language models (LLMs).

A frequent question is: “What is SERP in digital marketing?” It’s the page of results Google shows—snippets, FAQs, videos, and now AI summaries. Another is: “What are the characteristics of LLM?” These models rely on semantic understanding and contextual reasoning, not just ranking signals.

This raises: “How is LLM used in technology?” Beyond chat, LLMs are shaping search, discovery, and decision-making by selecting which brands appear in AI-powered answers.

To stay visible, brands must align with both SERP optimization and LLM visibility principles. Emerging AI assistants like KIVA show how structure, trust, and formatting decide what content gets surfaced.

For lean teams, the AI SEO Agent for Startups solution offers a direct path to scale SEO with SERP + LLM optimization built-in.

How SERP Visibility Drives AI-Influenced Search Performance?

SERP feature optimization has evolved beyond simple blue links as search engines now display featured snippets, AI Overviews, and People Also Ask boxes, blending traditional search results with generative interfaces.

This means visibility requires more than ranking—it’s about aligning your strategy to develop content tailored for Google’s SERP and building formats that match query intent.

What does ‘create for SERP’ mean?

To create for SERP is to design content that is both search-friendly and AI-aware. It involves steps like:

• Adding meta elements such as meta tags for SERP optimization.
• Writing keyword-aligned articles that build keyword-focused content for search engines.
• Implementing schema and markup to produce structured data for SERP enhancements.

Creating for SERP and Large Language Models

True optimization goes beyond Google. Marketers now face the challenge of creating for SERP and large language models simultaneously—which is why many are replacing spreadsheets and SOPs with AI agents that integrate SERP and AI-driven visibility workflows. That means designing for search engine results pages and LLMs, or even developing content for SERP and LLMs, so your assets appear in both blue links and AI-generated responses.

SERP Visibility Goes Beyond Ranking

Ranking high is only effective if your format matches searcher expectations. Pages that show up in featured snippets, PAA boxes, or AI summaries often follow specific patterns:

Focusing only on keywords while ignoring these format signals often creates broader SEO visibility issues, where content fails to perform across both Google SERPs and AI-driven summaries.

Analyze SERPs to Extract Content Opportunities

Manual audits don’t scale, but automated SERP tools (SEOTesting, Semrush’s SERP Features, Ahrefs’ SERP Overview, BrightEdge DataCube, SE Ranking’s SERP Checker) plus optimization platforms (Clearscope, MarketMuse, SurferSEO, ContentKing) can uncover gaps.

To scale your workflow after identifying these patterns, explore the blog 5 Tips to Triple Content Output Using AI Writing Assistants, which explains how AI-driven workflows can help you act on SERP insights faster and generate search-aligned drafts at scale.

Turn SERP Visibility into Content Briefs

Every SERP pattern should inform your content brief. Before writing:

For a practical walkthrough on this process, see how to create an AI content brief using KIVA showing step-by-step how SERP insights are transformed into structured, AI-aware outlines that perform across both search engines and generative platforms.

Why Large Language Models Extract Content Through Semantic Analysis?

AI content selection patterns reveal unique behaviors. Large language models like ChatGPT-4, Claude-3, and Gemini Pro don’t rank pages like traditional search algorithms. Instead, they use transformer architectures and attention mechanisms. These models employ semantic chunking, passage retrieval, and contextual analysis to identify and remix content blocks.

These systems prioritize semantic relevance, clear structure, and trusted citations. Unlike Google, LLMs don’t rely on traditional ranking signals like backlinks or keyword density. Instead, they extract information based on clarity, contextual fit, and answer quality.

To better understand how these AI-driven citations compare to traditional SEO link-building, take a look at How Are LLM Citations Different from Backlinks? where we break down the shifting role of trust and authority in generative search.

To explore this further, check out Why Generative Engines Love Reddit? for a breakdown of why forums dominate AI citation logic and how you can adapt your strategy to benefit from similar formats.

This modular approach improves extractability, increasing the chance that an AI model will pick and cite your content.

To apply this approach consistently, explore our guide on Chunk optimization for AI SERPs, where we break down how to label, format, and structure content blocks for maximum visibility across both search and AI interfaces.

How Content Alignment Maximizes SERP and LLM Visibility?

Multi-platform content visibility requires semantic long form content creation strategies that align with both SERP optimization and LLM citation best practices through a methodical approach. Each article must match how people search and how platforms select, display, and summarize content.

A step-by-step workflow ensures that each part of the article meets the expectations of Google Search and LLM tools like ChatGPT or Gemini.

Step 1 – Perform Topic and Query Research

Effective research begins by identifying what the audience is already searching for. Query-based research helps structure content around real demand rather than assumptions.

AI keyword research platforms reveal important patterns. Tools like KIVA, Semrush, Ahrefs, and AlsoAsked identify phrasing patterns and snippet formats. Content optimization tools like Clearscope, MarketMuse, and SurferSEO analyze ranking content structures. Analytics platforms including Google Search Console and Adobe Analytics provide performance data.

Matching keyword intent and question phrasing improves discoverability across both SERPs and LLM outputs, especially when guided by AI SEO agents using SERP visibility, which surface query patterns and content structures that rank in search and get cited in AI responses.

Step 2 – Build a Clear Content Outline

A clear content outline helps structure the article into answer-first sections. Google Search and LLM tools both favor writing that solves a problem upfront and supports the answer with details.

This structure helps guide both readers and search engines through the topic in a logical, intent-aligned flow.

Step 3 – Write With Structure and Simplicity

Google Search prioritizes content that is easy to scan and understand. LLM tools extract content from pages that lead with the answer and minimize ambiguity.

Writers should use formatting that signals clarity.

Writing should rely on the active voice, neutral tone, and sentence lengths between 15–20 words for consistent readability. Overuse of transitional phrases or introductory filler should be avoided.

Step 4 – Include FAQs and Supporting Information

Frequently asked questions improve SERP presence and help language models understand the full scope of the topic.

These sections often appear as AI-generated answers, Google’s “People Also Ask” boxes, or FAQ schema-enhanced listings.

To create high-impact FAQ sections, it’s important to align with real user intent. One effective approach is using People Also Ask data, which provides actual search queries you can turn into precise, snippet-ready answers.

According to Google’s Search Central (Webmaster Trends Team, 2023), properly marked-up FAQ sections using FAQPage schema may be displayed as rich results in search listings or Google Assistant, helping users find answers directly in search. [/did_you_know]

Step 5 – Format the Article for Indexing and Reuse

Content formatting affects how Google Search ranks the article and how LLM tools extract and display information.

Articles must be built for scanning, understanding, and retrieval at both page-level and section-level. Using clear headers, structured data, and modular sections makes content more reusable and visible.

Formatting should make every section function as a self-contained answer. When the reader (or  a machine) lands in the middle of the article, the section should still make sense without scrolling.

How KIVA Maps Visibility Across Search Engines and LLMs

Modern content strategy requires more than keyword targeting. It depends on understanding how both search engines and large language models (LLMs) interpret, structure, and present information.

KIVA by Wellows introduces a unified visibility framework designed to solve this challenge. It enables teams to move beyond isolated SEO tactics and embrace a connected, AI-first approach.

One of the core capabilities is the KIVA SERP Visibility feature. It shows which content formats dominate Google results—such as how-to guides, UGC, product roundups, or videos, so you can structure briefs aligned with real SERP behaviors.

The other is the KIVA LLM Visibility feature. It analyzes how models like ChatGPT, Claude, and Gemini interpret phrasing, structure, and sources—helping your team adapt content for AI citation and summary patterns.

For teams wanting to scale these capabilities into broader automation, the guide on Marketing With Agentic AI shows how KIVA connects SERP and LLM insights into an autonomous execution framework.

Step 1: Analyze SERP Behavior with KIVA’s SERP Visibility

KIVA content optimization tool provides SERP visibility enhancement that goes beyond traditional keyword rankings to analyze search result optimization opportunities.  It delivers a live breakdown of how your topic appears in search, including dominant content formats, layout structures, and competitor presence.

This enables content teams to create briefs that reflect real search behavior. As a result, the content matches what Google currently ranks and what users expect to find.

Step 2: Understand LLM Citation Patterns with KIVA’s LLM Visibility

While SERP data shows what people click, LLMs like ChatGPT, Claude, and Gemini reveal what content is cited or summarized. KIVA’s LLM Visibility feature analyzes how leading AI models interpret your topic, revealing phrasing logic, source preferences, and output structure.

It also provides model-specific visibility snapshots to show where you’re winning, where you’re missing out, and what opportunities exist for content improvement.

Why LLM Tools Prioritize Structured Content Selection?

LLM tools such as ChatGPT, Perplexity, and Google’s AI Overviews generate summaries by scanning public content and extracting sections that are clear, direct, and structurally consistent.

Articles that follow logical heading hierarchies, answer user questions upfront, and use concise language are more likely to be quoted, summarized, or linked.

LLM Tools Prefer Question-Based Sections and Predictable Structure

Language models are built to answer natural language questions. Articles that use subheadings in the form of complete queries, such as “How do search engines identify structured content?” These are easier for LLMs to understand and repackage.

LLM tools scan headers, then check whether the paragraph that follows provides a clear and relevant answer.

To increase the chance of selection:

• Phrase H2s and H3s as real questions
• Place the answer in the first 2–3 lines after the heading
• Limit technical terms unless followed by short definitions

In the case of the KIVA ChatGPT Visibility feature, these patterns are mapped directly into your content strategy—showing how structured questions, bullet points, and concise answers improve your chances of inclusion in AI-generated summaries.

Clarity, Simplicity, and Sentence Structure Affect Extraction Quality

Content selected by LLM tools often shares specific patterns:

• Sentences average 15–20 words
• Factual tone and active voice dominate the section
• Entities are clearly named and described (e.g., “Semrush is a keyword analysis platform…”)

Excessive use of filler phrases like “actually,” “essentially,” or vague openers such as “this means that…” weakens the section’s extraction potential. Clarity is measured not only by grammar but also by how quickly the core answer is presented.

The KIVA Claude Visibility feature emphasizes this, showing how Claude prioritizes clean editorial tone, accurate attribution, and well-organized passage blocks with minimal promotional language—especially for professional or knowledge-driven topics.

LLM Tools Use Content Blocks, Not Whole Pages

Language models do not typically summarize full articles. Instead, they extract standalone sections, often focusing on the content beneath individual H2 or H3 headings, to answer specific user queries.

This behaviour makes modular writing essential. Writers must ensure that:

• Each section works independently
• Sentences refer to the subject by name, not pronouns
• Internal references (e.g., “the above section”) are avoided

The KIVA Gemini Visibility feature favors cleanly chunked content, especially when structured with metadata, headers, and schema that guide how information is interpreted and grouped by the model.

Meanwhile, the KIVA DeepSeek Visibility feature shows a strong preference for forum-style language, community-sourced opinions, and context-rich responses, making it ideal for brands leveraging UGC and experience-based narratives.

When every section contains enough context to stand alone, that section becomes eligible for direct inclusion in AI Overviews, summaries, or assistant-style tools.

How Different Business Types Require Tailored Implementation Strategies

While the core of SERP and LLM visibility remains universal, how you apply it varies based on team size, workflow speed, and client pressure.

Agencies, consultants, and startups each face unique content challenges—and need scalable ways to execute strategy fast without sacrificing results.

For Agencies – Scale AI-Optimized SEO Briefs Across Clients

Content visibility strategies for agencies require managing dozens of clients across industries while implementing SERP and LLM optimization techniques under tight deadlines. That means your systems must adapt quickly to shifts in SERP formats or LLM citation trends.

Agency action points:

• Use content planning platforms that integrate real-time SERP snapshots and LLM query simulation.
• Standardize modular brief templates that reflect both keyword intent and model-extracted phrasing.
• Report client visibility across Google and AI channels with tools like AlsoAsked or LLM coverage tracking.

For Consultants – Translate Visibility into Strategy

Independent consultants need to prove results with fewer resources. Instead of manually checking Google and GPT responses, use a systemized visibility matrix —the same AI-first approach used to scale SEO as a solo consultant.

Consultant action points:

1. Analyze SERP types and AI citations per topic before pitching content.
2. Align deliverables with both AI-friendly structure and human-first value.
3. Use modular brief sections to plug into broader brand or editorial systems.

This builds trust with clients who are increasingly aware of AI’s role in content discovery. It also reinforces that SEO without a team is not only possible, but highly effective when powered by the right visibility insights and strategic frameworks.

For Startups – Move Fast Without Guesswork

Startups need early visibility, but often lack bandwidth for deep SEO audits or custom AI analysis. Startups use AI for SEO to close that gap—modular content helps them scale smarter, faster.

Startup action points:

• Use hybrid research (SERP + LLM) to find “low-content” opportunities.
• Build evergreen clusters using repeatable formats (FAQs, how-tos, comparisons).
• Repurpose chunks for social, email, and support docs.

To accelerate execution, startups can tap into frameworks like the KIVA AI SEO Agent, which distils AI behaviour and search data into actionable SEO structures.

It helps content teams understand what search engines rank and what AI models cite—without the need for manual audits or fragmented tooling.

That’s also why many startup teams are increasingly relying on content-specific AI agents—here are 10 reasons writers are turning to AI to simplify execution without sacrificing clarity or structure.”

FAQs

Final Thought: Get Found Where It Counts

Today’s visibility is no longer just about ranking. It’s about relevance across every discovery moment.

Whether a user types into Google or prompts an AI assistant, your content needs to show up clearly, confidently, and consistently. That requires purposeful structure, alignment with real search behavior, and content that speaks to both humans and machines.

The creators and brands who master this balance will be the ones who rise above the noise.