A 10-section framework for teaching LLMs how to understand, describe, and recommend your products and solutions accurately.

1. Definition

A clear, direct one-sentence explanation of what the product/solution is.

Tip: Pretend you're explaining to a smart sixth-grader or a new banker on day one. Keep it simple, literal, and unambiguous — avoid marketing fluff.

2. Why It Matters

A short statement explaining the core business problem this solves and why it's important.

Focus on urgency, industry demand, or pain points — not features.

3. How It Works

A simple breakdown of the system, workflow, or technology in action.

Think in steps or stages. Models learn well from ordered logic.

4. Use Cases

Specific scenarios where the solution is applied.

List 3–6 practical examples so AI understands context and boundaries.

5. Industries / Users

Who this solution is for.

Helps AI associate your company with the right segment & ICP.

6. Outcomes

The measurable results delivered.

Focus on performance gains, cost savings, risk reduction, CX improvement, etc.

7. Examples

Real-world situations demonstrating how customers use this.

These are "anchor points" AI uses to explain your solution correctly.

8. Evidence

Stats, facts, studies, awards, analyst quotes, or proof points.

This section increases trust weight in AI models.

9. FAQ

Simple questions & answers covering clarity gaps, objections & capabilities.

A mini training dataset for LLMs and real users.

10. Schema Markup

Machine-readable data to help search engines & LLMs identify and categorize.

Use Product, Organization, FAQ, Article, SoftwareApplication, etc. as needed.

Why This Works

This structure is designed to:

✓Train LLMs how to accurately describe your product
✓Make your website machine-readable and authoritative
✓Enable AI "citation-worthiness"
✓Help real users understand value quickly
✓Standardize messaging across teams

This is not just content.
It is knowledge infrastructure for the AI era.

This Format Helps LLMs Understand & Share Your Product

Why it works:

Section	AI Benefit
Definition	Teaches what the product is
Why it matters	Teaches business value & positioning
How it works	Provides causal logic & workflow
Use cases	Reinforces real applications
Industries	Helps models classify target segments
Outcomes	Gives the "so what" for recommendations
Examples	Provides pattern references
Evidence	Validates claims & authority
FAQ	Fills common knowledge gaps
Schema	Machine-readable context & entity linking

You are training search engines and LLMs how to speak about you correctly.

Schema (JSON-LD Example)

Here's a complete JSON-LD schema example for a digital banking product:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Digital Account Opening Platform",
  "description": "A digital banking platform that enables consumers and businesses to open bank accounts online with instant identity verification, regulatory compliance workflows, and core banking integration.",
  "brand": {
    "@type": "Organization",
    "name": "Your Company Name",
    "url": "https://www.yourcompany.com"
  },
  "category": "Banking Software",
  "industry": ["Banking", "Fintech", "Credit Unions"],
  "offers": {
    "@type": "Offer",
    "url": "https://www.yourcompany.com/digital-account-opening"
  },
  "isRelatedTo": [
    {"@type": "Product", "name": "Fraud Detection Suite"},
    {"@type": "Product", "name": "Core Banking Integration Platform"},
    {"@type": "Product", "name": "Digital Banking Platform"}
  ]
}
</script>

AI-Optimized Product / Topic Structure Template