How to Structure Your App Landing Page for LLMs

Your app landing page serves two audiences now: human visitors making install decisions and AI systems deciding whether to recommend you.

These audiences have different needs. Humans scan for social proof and emotional resonance. LLMs scan for semantic clarity and structured information.

The good news: a well-structured landing page serves both. Clear value propositions, logical information hierarchy, and comprehensive use case documentation work equally well for conversion optimization and AI discovery.

Here's the optimal structure for maximum visibility in both contexts.

Above the Fold: The Critical First 200 Words

LLMs prioritize content that appears early on the page. Your first 200 words carry disproportionate weight in how AI systems understand your app.

Essential elements above the fold:

1. Clear value proposition (H1) One sentence that states what your app does and who it's for.

Example: "Track expenses and manage budgets for freelancers and small businesses"

2. Target user specification Be explicit about who this is for.

Example: "Built for self-employed professionals managing business and personal finances"

3. Core problem statement What problem does this solve?

Example: "Stop overspending, prepare for taxes, and maintain cash flow visibility"

4. Primary use case One specific way users accomplish their goal.

Example: "Automatically categorize expenses, set spending limits by category, and see real-time budget progress"

Why this matters:

When an LLM processes your page, it extracts key concepts from early content to create an embedding that represents your app. If your first 200 words are vague marketing copy or visual elements without text, the embedding will be weak.

Front-loading semantic clarity ensures accurate categorization.

Section 1: Features with Semantic Clarity

After the hero section, detail your core features using structure that LLMs can parse.

Optimal features section structure:

<section>
  <h2>Features</h2>
  
  <article>
    <h3>Automatic Expense Tracking</h3>
    <p>Connect your bank account and credit cards to automatically import and categorize transactions. See where your money goes without manual data entry.</p>
  </article>
  
  <article>
    <h3>Budget Planning by Category</h3>
    <p>Set monthly spending limits for categories like groceries, dining, and entertainment. Get alerts when you approach limits to prevent overspending.</p>
  </article>
  
  <article>
    <h3>Cash Flow Forecasting</h3>
    <p>Project future income and expenses based on historical patterns. Plan for irregular income months and large upcoming expenses.</p>
  </article>
</section>

Key principles:

Use <article> tags for each feature to signal self-contained content blocks

Use H3 headings for feature names that are descriptive, not clever

Explain what the feature does, not just what it's called

Include the problem it solves in the description

Use action verbs: "Track expenses" > "Expense tracking module"

This structure allows LLMs to extract individual capabilities and understand when each is relevant.

Section 2: Use Cases for Intent Matching

LLMs match apps to user intent. Documenting specific use cases makes it clear when your app is relevant.

Use case section structure:

<section>
  <h2>Use Cases</h2>
  
  <article>
    <h3>For Freelancers: Separate Business and Personal Spending</h3>
    <p>Tag transactions as business or personal. Generate expense reports for tax time. Track deductible expenses automatically.</p>
  </article>
  
  <article>
    <h3>For Variable Income: Budget with Irregular Paychecks</h3>
    <p>Set percentage-based budgets that adjust with income. Prioritize essential expenses. Build buffer savings for low-income months.</p>
  </article>
  
  <article>
    <h3>For Couples: Shared Budget Management</h3>
    <p>Connect multiple accounts. See combined spending. Set joint savings goals. Track who spends what while managing shared finances.</p>
  </article>
</section>

Why specific use cases matter:

When a user asks ChatGPT "What app helps freelancers track business expenses for taxes?" the LLM scans for apps that mention this specific use case.

Generic feature lists don't match specific intents as well as documented use cases.

Section 3: Problem-Solution Pairs

Explicitly documenting problems and solutions creates semantic hooks for LLMs.

Problem-solution structure:

<section>
  <h2>Problems We Solve</h2>
  
  <article>
    <h3>Problem: "I don't know where my money goes each month"</h3>
    <p><strong>Solution:</strong> Automatic transaction categorization shows exactly how much you spend in each area. Visual breakdowns reveal spending patterns you didn't know existed.</p>
  </article>
  
  <article>
    <h3>Problem: "I keep overspending my budget"</h3>
    <p><strong>Solution:</strong> Real-time budget tracking with alerts helps you stay aware of limits. See remaining budget in each category before making purchases.</p>
  </article>
</section>

This format teaches LLMs exactly which user problems your app addresses.

Section 4: How It Works

Process documentation helps LLMs understand your workflow and user experience.

How it works section:

<section>
  <h2>How It Works</h2>
  
  <ol>
    <li>
      <h3>Connect Your Accounts</h3>
      <p>Securely link bank accounts and credit cards using bank-level encryption. We support 10,000+ financial institutions.</p>
    </li>
    <li>
      <h3>Transactions Automatically Import</h3>
      <p>New purchases appear within 24 hours. Our AI categorizes them based on merchant and past patterns.</p>
    </li>
    <li>
      <h3>Set Budgets and Goals</h3>
      <p>Define monthly spending limits by category. Create savings goals with target dates and amounts.</p>
    </li>
    <li>
      <h3>Track Progress in Real-Time</h3>
      <p>See current spending vs. budget limits. Get alerts before you exceed limits. Review weekly summaries.</p>
    </li>
  </ol>
</section>

Ordered lists with H3 headings create structured process information that LLMs can extract and reference.

Section 5: FAQs with Schema Markup

FAQ sections serve double duty: addressing user questions and providing structured Q&A for LLMs.

FAQ implementation:

<section>
  <h2>Frequently Asked Questions</h2>
  
  <article itemscope itemprop="mainEntity" itemtype="https://schema.org/Question">
    <h3 itemprop="name">Is my financial data secure?</h3>
    <div itemscope itemprop="acceptedAnswer" itemtype="https://schema.org/Answer">
      <p itemprop="text">Yes. We use bank-level 256-bit encryption and never store your banking credentials. We're SOC 2 Type II certified and undergo annual third-party security audits.</p>
    </div>
  </article>
</section>

Accompany with JSON-LD schema:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "Is my financial data secure?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Yes. We use bank-level 256-bit encryption..."
    }
  }]
}

FAQ schema is one of the most-cited structured data types in AI responses.

Section 6: Social Proof with Structured Data

Reviews and ratings provide third-party validation that LLMs trust.

Review section with schema:

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Your App",
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.8",
    "ratingCount": "12450",
    "bestRating": "5",
    "worstRating": "1"
  }
}

Include actual user reviews with specific use cases mentioned:

"This app helped me save $500/month by showing where I was overspending" is more valuable than "Great app!"

Section 7: Pricing with Offer Schema

Explicit pricing information with structured data helps LLMs understand your business model.

Pricing schema:

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Your App",
  "offers": [
    {
      "@type": "Offer",
      "name": "Free Plan",
      "price": "0",
      "priceCurrency": "USD"
    },
    {
      "@type": "Offer",
      "name": "Pro Plan",
      "price": "9.99",
      "priceCurrency": "USD",
      "priceValidUntil": "2025-12-31"
    }
  ]
}

Content Hierarchy Best Practices

One H1 per page: Contains your primary value proposition

Multiple H2s for major sections:

• Features
• Use Cases
• How It Works
• Pricing
• FAQs

H3s for subsections within each:

• Specific features
• Individual use cases
• Process steps
• Individual questions

Use semantic HTML:

• <section> for major content blocks
• <article> for self-contained items
• <strong> for important terms
• <em> for emphasis

Visual Elements That Help LLMs

Annotated screenshots: Include captions and alt text that explain what's shown

Example:

<figure>
  <img src="dashboard.png" alt="Budget dashboard showing monthly spending by category with progress bars indicating remaining budget in each area">
  <figcaption>See real-time budget progress across all spending categories</figcaption>
</figure>

Video with transcripts: App preview videos should include text transcripts that LLMs can parse

Icons with labels: Always pair icons with text labels. LLMs can't reliably interpret icons alone.

Meta Information

Title tag: Include value proposition and target user "BudgetApp - Expense Tracking and Budget Management for Freelancers"

Meta description: Expand on what and who "Track business expenses, manage budgets, and prepare for taxes. Built for freelancers and self-employed professionals with variable income."

Canonical URL: Use semantic, descriptive URLs yourapp.com/expense-tracking-freelancers

Mobile Considerations

LLMs increasingly prioritize mobile-optimized content.

Mobile optimization:

• Responsive design that works on all screens
• Same content on mobile and desktop (no separate mobile site)
• Touch-friendly navigation
• Readable without zooming
• Fast load times

Content Length and Depth

Optimal landing page length for AI discovery: 2,000-4,000 words

Why longer is better:

LLMs favor comprehensive resources that thoroughly cover a topic. A landing page that includes features, use cases, how-to guidance, FAQs, and detailed problem-solution pairs gives the LLM more semantic material to work with.

Shallow pages with minimal content are less likely to be confidently recommended.

FAQs

What should be in the first 200 words of an app landing page?

The first 200 words should include your value proposition, target user, core problem solved, and primary use case. LLMs prioritize early content when building understanding, so front-load your most important semantic signals.

How should I structure headings for AI discovery?

Use one H1 containing your main value proposition. Use H2s for major sections like Features, Use Cases, and Pricing. Use H3s for specific capabilities within each section. This hierarchy helps LLMs extract structured information about your app.

Should app landing pages be long or short?

For AI discovery, comprehensive pages perform better than minimal ones. Include detailed use cases, features, FAQs, and problem-solution explanations. LLMs favor pages that thoroughly cover a topic over shallow landing pages.

Do I need different landing pages for humans vs AI?

No. The same principles that help LLMs understand your app—clear value propositions, logical structure, specific use cases—also improve human comprehension and conversion rates. Optimizing for one audience improves results with both.

How often should I update my landing page?

Review and refresh every quarter. Update metrics (user counts, ratings), add new use cases as you discover them, expand FAQs based on common questions, and ensure all information remains current and accurate.

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A well-structured landing page serves human visitors and AI systems simultaneously. Clarity, comprehensiveness, and semantic precision benefit both audiences.