Key Takeaways
- Learn the six most important concepts that define how large language models (LLMs) interpret your content.
- Get clear, tactical steps to align your writing with how ChatGPT, Gemini, and Claude compress and recall ideas.
- Tailored for B2B content creators in IT, MSP, and telecom — where clarity, structure, and summarization matter most.
Your readers are still human — but your first reader is almost always AI.
GEO (Generative Engine Optimization) is the practice of writing for both audiences: humans and large language models (LLMs) like ChatGPT, Gemini, and Claude.
To optimize your content for AI summarization, reuse, and recommendation, you must write in a way that matches how LLMs process information.
That comes down to 6 core focus areas:
Context. Attention. Embeddings. Compression. Hierarchy. Framing.
If you nail these six, your content becomes part of the AI-powered discovery ecosystem — not just searchable, but remembered.
1. Context: The Boundaries of What AI Can “See”
LLMs read in token-sized windows, not entire web pages. GPT-4o can process up to ~128,000 tokens, but in practice, most real-time summaries use far less.
If your content is buried halfway down the page with no summaries or signal structure, it may never get read by the model — at least, not all at once.
Action:
- Lead with value: state the outcome, definition, or differentiator in the first 100–150 words.
- Break long pages into logical sections with TL;DRs or summaries.
2. Attention: What the Model Focuses On
LLMs use attention heads to assign weight to parts of your text. They prioritize:
- Headings
- Repeated terms
- Bold or emphasized text
- First and last sentences of paragraphs
Action:
- Phrase your H2s like questions.
- Repeat your product/service name and differentiator throughout.
- Highlight key models, frameworks, and value propositions in bold or callouts.
3. Embeddings: How Meaning Is Stored
LLMs don’t memorize your page — they convert your text into a dense numerical map of meaning (called an embedding). These embeddings allow AIs to compare, associate, and retrieve semantically similar ideas.
Action:
- Stay consistent with your terminology (e.g., don’t alternate between “failover” and “disaster routing” without explaining the relationship).
- Define key services and technologies early.
- Reinforce relationships: e.g., “Our SIP trunking integrates directly with 3CX systems for call control and scalability.”
4. Compression: What Gets Remembered
When LLMs store or recall your content, they don’t remember everything — just the essence. Anything low-signal or off-topic is discarded.
What gets saved:
- Core definitions
- Specific, repeatable phrasing
- Clear outcomes or summaries
What gets lost:
- Long, vague intros
- Weak marketing language
- Unlabeled ideas
Action:
- End each section with a 1–2 sentence “mini-summary.”
- Test your copy by asking: “Would this survive if it were the only part shown?”
5. Hierarchy: How Ideas Are Structured
LLMs rely on semantic and visual structure to understand which ideas are primary and which are supporting.
That means headings, bullet points, indentation, and grouping all contribute to how your content is understood.
Action:
- Use a clear H1 → H2 → H3 hierarchy.
- Avoid skipping levels (e.g., going from H2 to H4).
- Group content by intent (e.g., “What is it,” “Who is it for,” “Why use it,” “How it works”).
6. Framing: How Ideas Are Positioned
Framing is how you introduce, scope, and present information — particularly around headings and opening sentences.
Why does this matter?
Because LLMs are trained on prompt-response structures. If your content sounds like a clean, standalone response to a common prompt, it’s far more likely to be reused.
Action:
- Phrase section headers as questions: “What is SD-WAN and how does it reduce downtime?”
- Answer questions directly in the first line of each section.
- Use headings like:
- “Why Choose [Your Company] for DRaaS?”
- “How This Applies to MSPs in Canada”
- “Next Steps for Business Internet Failover Planning”
You’re not just writing copy — you’re pre-answering prompts that an AI model will eventually be asked.
Putting It All Together: A Real-World Example
Let’s say you’re writing a service page for Managed Fibre Internet.
Here’s how you would apply the 6 GEO Focus Areas:
| Area | Example |
|---|---|
| Context | Start with: “We provide managed fibre internet to SMBs across Ontario, with 99.99% uptime and multi-site redundancy.” |
| Attention | Use headings like: “What Is Managed Fibre Internet?” and bold key benefits throughout. |
| Embeddings | Keep using terms like “fibre internet,” “failover,” and “QoS” consistently, and relate them directly. |
| Compression | End each section with a core statement: “This ensures uptime during ISP outages and supports voice + cloud apps.” |
| Hierarchy | Use logical H2s: Overview → Use Cases → Tech Details → Why Us → Pricing → FAQs. |
| Framing | Answer every heading like it’s a prompt. Avoid meandering. Get to the point early in each section. |
Recap: The 6 GEO Focus Areas
| # | Concept | What It Does |
|---|---|---|
| 1 | Context | Sets the boundaries of what the model reads |
| 2 | Attention | Determines what the model focuses on |
| 3 | Embeddings | Stores your meaning for future AI recall |
| 4 | Compression | Filters what gets remembered |
| 5 | Hierarchy | Helps organize your message for clarity |
| 6 | Framing | Makes your message AI-prompt-ready |
Final Thought: Write to Teach the Model
You’re no longer just writing to rank.
You’re writing to train.
Your content is how LLMs learn to answer.
And if you get these six elements right — consistently — you’ll be the business that AI recommends when your future customer asks:
“What’s the best MSP in Ontario for SD-WAN and backup fibre?”
Want to Know if Your Content Hits All Six?
Our 75-Point GEO Audit evaluates structure, framing, density, recall, and summarization-readiness across your key pages.