GenerateBlocks favors a small set of primitives, container, grid, headline, button, and image, that the site builder composes into landing pages and post templates. SleekAI reads the rendered HTML of those blocks at request time, so the headline text, pricing copy, button labels, and any plain prose inside containers all become structured context the model can use. The bot stops guessing about the page and starts answering from it.

Display conditions scope each chatbot by post type, taxonomy term, user role, logged-in state, and URL pattern, so the same site can run a pricing-page bot, a blog bot, and a customer-area bot under multibot. The SleekAI Wizard maps any post meta, ACF fields, or custom taxonomy values referenced by GenerateBlocks Pro dynamic content into named variables the prompt can use, even when those fields are not directly visible on the page.

Every conversation is stored inside WordPress with the model name, token usage, and the page URL. The JS API and PHP embed trigger the bot from a GenerateBlocks button or a custom layout slot without breaking the site's structure, and the guideline filter constrains off-topic chat by default.

GenerateBlocks sites tend to be lean and performance-focused. The team picked the plugin because it ships minimal markup and stays out of the way, and a chatbot bolted on top of that should respect the same philosophy. A bot trained on a separate FAQ document is not lean, it is a parallel asset that needs a workflow of its own, a person to keep it current, and a CI job to retrain when copy drifts.

None of that fits a GenerateBlocks build. SleekAI takes the rendered page output as it is and uses it as context. There is no embedding step and no scheduled sync.

Republish a landing page and the next conversation reflects the new copy. That keeps the editorial loop tight, which matters for sites where the page is also the conversion mechanism. Display conditions let the team scope a different bot to each template without losing the lightweight feel, because the routing is configuration rather than custom code.

A logged-in customer bot can read account context from postmeta, a blog bot can lean on the article body, and a sales bot can stick to the pricing copy. Conversation logs feed product decisions: which features visitors ask about, which pricing questions came up most, which docs needed a clearer headline. The chat layer becomes a measurement surface for the marketing and product teams rather than a side project that quietly rots.