LLM Content Optimization Checklist

B2B search is undergoing a foundational shift, and many companies aren’t ready.

Buyers are increasingly turning to tools like ChatGPT and Claude instead of traditional search engines to get answers.

These models don’t return links — they generate synthesized, conversational recommendations. If your content isn’t being referenced, it’s not part of the buyer journey.

At Virayo, we’re getting ahead of that shift.

We’re testing how LLMs interpret, filter, and surface content, so your business shows up where buying decisions now begin.

Many of our SaaS clients are seeing growth in traffic and trials/demos from LLM platforms:

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This guide is a working document: a behind-the-scenes look at what we’re learning, testing, and applying to help you stay ahead of the curve.

Learn how to:

• Create content that ranks and gets referenced by AI
• Build structured, trustworthy content blocks for LLMs
• Show up in AI-generated answers, not just blue links

What does this shift look like in practice?

A need to rethink how content gets discovered, understood, and cited in this new environment.

Let’s start by comparing the old playbook — traditional SEO — with the new one: content built to be understood and cited by large language models.

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Foundations of GEO and LLM visibility

Intent In The LLM Era

Old SEO:

“Best CRM for small teams”

→ Search → Click → Funnel

Now:

“We’re missing handoffs between SDRs and AEs”

→ Claude recognizes intent → Generates curated answer

LLM Intent Defined:

Intent = signals that someone (or their AI) is actively trying to solve a problem or reach a goal.

It lives in:

• Friction-filled workflows
• Conversational queries
• Prompt patterns (not just search)

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How to Map Content to LLM Intent

Based on your "Intent in GEO" post, OpenAI prompt behavior, Claude synthesis patterns

Understanding intent is step one. Knowing how models actually retrieve and cite content, and what they look for,  is just as important

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How LLMs Retrieve and Cite Content

LLMs don’t “rank” — they retrieve and synthesize. Your content is selected based on:

Retrieval Behavior

Claude + OpenAI Recommended

Once you understand how LLMs select information, the next question is: what kinds of content get picked?

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Content Formats That Win

Claude and Google AI Overviews inspired formats

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How To Write Quote-Worthy Content

Principles:

• Start with the answer
• Keep paragraphs 1–3 sentences
• Use branded, stat-backed claims
• Make each section usable on its own

Example (bad):

“There are many CRMs on the market with different features…”

Example (good):

“Agent Bloom reduced contract-to-close time by 42% within 60 days, according to an internal pilot.”

Style matters, but so does structure. To maximize visibility, your content needs to be technically optimized for how models chunk, crawl, and process information.

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Structural and Technical Requirements

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How To Track LLM Visibility

Tools:

• Peec.ai – monitors your brand in AI outputs
• Ziptie.dev
• Daydream – shows which phrases you’re cited for
• Perplexity + ChatGPT + Claude – run prompt tests

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Sample prompts to test:‍

• "Best [Use Case] software"
  
  • E.g. best field sales software
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• "Best [Use Case] software for [Role/Industry]"
  
  • E.g. "best TMS software for carriers"
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• "[Competitor] alternatives"
  
  • E.g. “NinjaOne alternatives”
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• "Competitor A vs Competitor B"
  
  • E.g. “HubSpot vs Pipedrive”
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• Jobs-to-be-done/ pain point questions
  
  • E.g. “How do I reduce handoffs between SDRs and AEs?”
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• “What are the best AI tools for [Industry]?”
  
  • E.g. What AI tool is best for real estate?”
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• “How do I improve field sales rep accountability?”

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Publishing Checklist

• Clear H1 title‍
  
  • Use a concise, keyword-rich H1 that clearly signals the topic and aligns with search intent.

• Primary keyword and brand in first 100 words
  
  • Mention the the primary keyword and brand as early as possible. This will guide both readers and search engines.
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• Structured headers (H2, H3)
  
  • Break content into logical sections using descriptive headers. Aim for semantic clarity that matches user queries. E.g. “Top benefits of using AI in [Industry]”
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• Short paragraphs
  
  • Keep paragraphs digestible to enhance scanability for readers and AI models.
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• Parsable answers to key questions
  
  • Ensure the content quickly and answers the core user question(s) near the top of the piece.
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• One idea per section
  
  • Avoid bloated sections. Each should convey a single, focused idea or argument.
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• Use of tables or lists
  
  • Include bullet points, numbered lists, or tables to visually organize data and support quick information retrieval.
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• Stats and branded statements
  
  • Support claims with up-to-date statistics and include differentiating brand messages or value propositions.
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• Frontload key insights
  
  • Deliver major takeaways in the first third of the content to engage users early and support snippet/summary use.
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• Semantic terms throughout
  
  • Incorporate semantically related phrases across headers and body copy to improve topical relevance and AI parsing.
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• FAQ or summary box
  
  • Add a TL;DR, takeaway box, or collapsible FAQ to highlight key answers and boost snippet potential.
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• E-E-A-T signals
  
  • Demonstrate Experience, Expertise, Authoritativeness, and Trustworthiness through bios, sourcing, and tone.
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• SME input and internal data
  
  • Where possible, include proprietary insights, SME quotes, or internal performance data to enhance originality.
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• Internal and external links
  
  • Link to authoritative external sources and relevant internal pages to build topical authority and SEO depth.
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• Schema and meta tags implemented
  
  • Ensure the proper meta description, Open Graph tags, and schema markup are included and relevant.
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• Minimize content noise
  
  • Avoid excessive popups, CTAs, embeds, or modals that clutter the experience and interfere with LLM interpretation.
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• Final proofread by a human
  
  • Complete a last-pass edit for clarity, grammar, and readability before publishing.

Key takeaways

• Design for humans and machines: Modern SEO requires writing that is clear, structured, and semantically rich, for both readers and large language models.
• Focus on intent and usability: Each section should answer a specific question or intent, and the content should deliver value upfront.
• Leverage brand and authority: Original insights, expert commentary, and clean linking structure can significantly enhance trust signals and differentiation.
• Use semantic cues for LLM visibility: Guide indexing and snippet extraction by using clear headers, formatting, and content hierarchy. (Reference)

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Looking to the Future

We’re in the early innings of a major shift in how content is found, understood, and used.

This guide reflects what we’re seeing now, but the landscape is evolving fast. LLM behavior, prompt patterns, and retrieval dynamics are changing weekly.

Staying ahead means staying adaptive.

We’ll keep learning in public. We’ll keep testing.

If you’re seeing something different — or trying something we haven’t covered — we’d love to hear from you. What’s working? What’s breaking? What’s unclear?

Let’s figure it out together.

If you'd like help improving your brand's discoverability across LLM platforms, schedule a strategy call here.

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