For this entire page we use a company selling coffee griders as example.
Level 1: Give the model a clear map
If you miss this, the model may understand the topic in a vague way, but it will struggle to extract anything useful.
Start with a clean hierarchy.
Ensure that your page uses headings (H1/H2/H3/H4) correctly so that the LLM easily can understand and group the content on your site. If you use other elements souch as tables, ensure these are either using the '<table>' format or that your '<div>' has the correct role set. If this sounds to technical, just send the sentance to your dev.
Front-load the summary.
The first two sentences on the page should read like a tight product brief, not a marketing pitch. For example: "This grinder uses 64 mm flat burrs, weighs 3.1 kg, and grinds an 18 gram espresso dose in 7.2 seconds. It offers 30 stepped settings that cover espresso, filter, and French press." That gives an answer engine something it can quote almost verbatim when a user asks about grinder specs.
Use declarative sentences that say one thing clearly.
"The burrs are made from hardened steel." "The grinder produces less than 1 gram of retention per dose." "The motor runs at 350 rpm under load." These are the lines the model will grab when it needs clean, factual output. If you bury those facts inside long, flowing paragraphs, the system has to work harder to slice them out.
Keep paragraphs short. A twelve sentence block about "overall performance" is a good way to have your most important details skipped. Break it up. One paragraph for grind speed, one for noise level, one for consistency measurements. If you need to present numbers, use simple formatting so each figure sits next to a label. For example, write "Grind time for 18 g at espresso: 7.2 seconds" as its own line instead of hiding it inside a paragraph about "fast performance".
Do this, and the model can quickly see what the page is about, where each topic begins and ends, and which sentences are safe to lift.
Level 2: Help the model pick you over the others
Once the basics are in place, you move into the midfield. At Level 2, the goal is to give answer engines reasons to prefer your grinder page when a question involves comparison or intent.
Structured comparisons
They matter because AI search has to justify recommendations. If someone asks "Which coffee grinder under 300 has the least retention", the model has to rank options and explain that ranking. A clear comparison block does that work in advance. Imagine a table on your page that lines up your grinder against a popular rival, column by column: grind time for 18 g at espresso, measured retention, noise in dB at one meter, price, warranty length. When the model sees that your grinder retains 0.6 g while a competitor sits at 1.4 g, it can quote that difference directly in an answer.
Content that matches search intent, not keyword theories
If people are typing "best grinder for small kitchens", you need a section that actually answers that. A paragraph that says "This grinder fits well in small kitchens because the 3.1 kg body is compact enough to move in and out of cupboards, and the 32 cm height clears most shelves" is far more useful than generic claims about "space saving design". If the query is "quietest grinder under 300", write a short section that names the noise level in dB, compares it with a typical cheap grinder, and states what that means in a real apartment at 6 a.m.
Scannable assets help both humans and models. A "Key facts" block near the top can condense the most important numbers into a few lines: runtime for a typical dose, burr size, noise, weight, warranty term. All of those can be read as atomic facts, then reused in different answer contexts.
FAQs have returned as a serious format
Answer engines already look for Q and A blocks that resemble real searches. On a grinder page, questions like "Does this grinder work for espresso", "Can it grind fine enough for Turkish coffee", or "Does it handle oily dark roasts" should each sit above a short, direct answer. For example: "Yes. This grinder can reach a grind size fine enough for espresso and includes a recommended starting range for common machines." Short, clean, no fluff.
With Level 2 in place, your grinder page is no longer just understandable to the model. It becomes a candidate whenever the question implies a choice, a use case, or a constraint.
Level 3: Give the LLM ready to use data and content
Level 3 is where you stop being a generic source and start acting as a reference. The model should be able to pull numbers from your page, defend them with your own justification lines, and cover multiple stages of the buying cycle without leaving your site.
Schema's
Schema.org has created a standardized data format that helps search engines and answer engines understand the content on your website. By adding specific tags to your website's code, you can tell search engines what the page is about, and give it structured data to consume.
These tags can be used for data such as product information, "Q&A" (questions & answers), customer feedback, technical product data. For websites that publish articles, it can also be used to add content summaries, author, and topic."
If your grinder page exposes price, burr size, burr material, dimensions, stock status, warranty terms, and energy usage through structured data, answer engines do not have to guess. When someone asks "What is the burr size of this grinder", the system can respond with your 64 mm figure rather than interpolating from a scraped paragraph. The same applies to attributes like "Noise level in dB", "Hopper capacity in grams", or "Maximum grind time before auto stop".
Justification-ready statements
AI systems often need to explain why a product appears in a recommendation instead of a rival. You can pre-write those justifications, as long as they are tied to verifiable facts. A grinder page might contain a line like: "This grinder has the longest verified stable grind time in its price range, maintaining a 7.2 second average for an 18 gram espresso dose over ten consecutive shots in lab tests run in 2024." A model can now quote both the claim and the basis for it when a user asks "Which grinder under 300$ keeps consistent speed".
Comparative justification sections work in the same direction. Instead of a vague "Why this grinder is great" heading, use something like "Why this grinder outperforms typical entry level burr grinders". Under that, spell out measurable differences: a clear percentage drop in retention compared with a cheap unit, a specific noise reduction figure, an extra year of warranty coverage. These lines act as ready-made reasons in ranking style answers.
Finally, think in layers of intent.
A grinder page will not only serve people asking "Which grinder should I buy". The same URL may be pulled into answers for "How do I clean a coffee grinder", "When should I replace grinder burrs", or "Why is my espresso suddenly channeling after six months". Separate sections for maintenance, troubleshooting, and upgrade advice keep your content in play across these follow up questions. A model can guide a user from "Which grinder is right for me" to "How do I look after it" to "Is it time to upgrade the burrs" without dropping you as the source.
