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.
