The way search engines read content has changed fundamentally over the last decade. The old model treated a page as a bag of words and rewarded the right keyword density. The current model uses natural language processing to understand meaning, structure, and usefulness. It reads a sentence the way a literate person reads it: does this say something true and useful, in well-formed language, about the thing the reader is looking for?

This change is good news for function-driven content done well, and fatal for function-driven content done badly. The keyword-stuffed, structurally identical filler that some generated content produces is exactly what modern NLP discounts. The specific, well-formed, genuinely informative sentences that conditional statements produce are exactly what it rewards. This article closes the Framework section by explaining how to land on the right side of that line.

What modern NLP rewards

Natural language processing in search has converged on rewarding a few properties that all point in the same direction: language that genuinely communicates.

It rewards well-formed sentences with real subjects, verbs, and objects, over fragments and keyword lists. "Choose from 412 tactical bipods starting at $24.50" is a well-formed sentence. "Tactical bipods cheap best buy online" is not. The first communicates; the second is keyword soup that NLP recognizes and discounts.

It rewards specificity, concrete nouns and numbers over vague generalities. "412 bipods from 38 brands" carries information. "A wide selection of quality products" carries none. NLP increasingly distinguishes between sentences that contain real information and sentences that only sound like they do.

It rewards action orientation, language that helps the reader do something. Verbs like choose, compare, shop, browse, build, find, and save signal that the content is oriented toward the reader's goal. These are the action words that, used naturally, make a sentence feel useful rather than descriptive.

It rewards relevance to intent, content that matches what the searcher was actually trying to accomplish. A category page that describes the category specifically, mentions the relevant brands, and helps the visitor navigate is matching intent. A category page with a generic paragraph about how important the product type is to the lifestyle is not.

How action words work in function-driven content

Action words are verbs that orient a sentence toward the reader's goal. In function-driven content, they are the connective tissue that turns a list of data points into a sentence that reads like guidance.

Consider the difference between these two ways of presenting the same data. The first is data without action words: "412 tactical bipods. 38 brands. From $24.50." That is accurate but reads like a database dump. The second wraps the same data in action words: "Choose from 412 tactical bipods across 38 brands, starting at $24.50." The action word "choose" turns the data into an invitation. The sentence now reads like it is helping the visitor do something.

The trick at scale is to build a vocabulary of action words appropriate to each page type and intent, then let the functions deploy them. A category page leads with "choose" or "browse." A comparison context uses "compare." A promotional context uses "save." A new-arrival context uses "discover." The functions select the action word that fits the data and the page type, producing sentences that read like guidance rather than data dumps, across thousands of pages.

Why action words matter to NLP

An action verb signals that a sentence is oriented toward helping the reader, which is exactly the usefulness signal NLP has been trained to reward. The same product data, presented as a bare list, reads as a database printout. Wrapped in the right action word, it reads as guidance. The data is identical. The framing determines whether NLP sees useful content or filler.

The evolution that makes this matter

It is worth understanding the history, because it explains why this approach is durable rather than a trick that will stop working.

In the mid-2000s, the prevailing wisdom was that content was king and volume mattered. Sites won by publishing more pages with the right keywords. By around 2010, the standard had risen to useful content, and the algorithms began rewarding pages that actually helped readers. Today, the standard is higher still: content must be specific, unique, and updatable to compete, and NLP enforces that standard by actually reading for meaning.

Each step in that evolution moved in the same direction: toward rewarding content that genuinely communicates useful information and away from content that games signals. Function-driven content built on conditional statements and action words is aligned with that direction. It produces specific, useful, well-formed sentences. As NLP continues to improve, content that genuinely communicates will continue to be rewarded. The approach is durable because it is aligned with what the algorithms are actually trying to measure, not with a loophole in how they measure it.

Where teams get this wrong

The failure mode is producing generated content that optimizes for the old model while modern NLP reads it with the new model. This happens when functions are designed to hit keyword density or to stuff category names repeatedly, rather than to communicate.

A function that produces "Tactical bipods - shop tactical bipods - best tactical bipods - tactical bipods for sale" is optimizing for a search engine that stopped existing a decade ago. Modern NLP reads that and sees keyword repetition with no informational content. It discounts the page. The function felt like SEO to whoever designed it, but it was SEO for 2008.

The correct approach designs functions to communicate. The output should read well to a human, because reading well to a human is now the same thing as reading well to the algorithm. If you would be slightly embarrassed to have a person read the sentence your function produces, NLP will discount it. If the sentence is one a knowledgeable salesperson might actually say, NLP will reward it.

The trap door

The simplest test for whether your generated content satisfies modern NLP is to read it aloud. If it sounds like something a helpful, knowledgeable person would say about the page, it will rank. If it sounds like keyword repetition or database output, it will not. Teams that skip the read-aloud test ship functions that feel like SEO and read like spam. The algorithm has been trained on the difference.

Closing the Framework section

The Framework section has built up the full machinery of function-driven content. Variables are the data in your database. Functions are the instructions that read variables and produce output. Shortcodes place the output on pages. Conditional statements make the output specific and varied. Page segmentation gives each page type the right treatment. And action words plus NLP awareness ensure the output reads like useful human writing rather than generated filler.

Put together, this machinery produces content that is specific, unique, updatable, and genuinely useful, at a scale no hand-writing team can match, in a form that modern search engines reward. That is the whole framework.

The next section, Tactical Application, gets concrete about which of these techniques apply to which specific page elements: title tags, captions, anchor text, social proof, freshness signals, and more. The framework is now in place. The rest is application.

From the book

The labeling and natural language processing chapter of Sizzle: An E-Commerce Revolution covers action words, the evolution of content standards, and how to design functions that satisfy modern NLP rather than the search engines of a decade ago.