Cut Nominalizations After Your AI Draft

AI drafts love nominalizations: the implementation of, the utilization of, consideration should be given to, optimization of workflow. Each phrase sounds professional while hiding who acts. A nominalization pass turns those noun stacks back into verbs and actors so tutorials read like steps—not like compliance PDFs.

Early-link cluster (run in order with sibling passes):

• Rewrite passive voice after your AI draft
• Cut filler phrases after your AI draft
• AI tight edit pass after draft
• Remove clichés after your AI draft
• AI content that doesn't sound fake: an editing workflow

This page is noun-to-verb clarity. It is not filler cutting (template phrases). It is not active voice alone (grammar role without unpacking -tion words). It is not length-first tight edit.

Draft → filler sweep → nominalization sweep → active voice where still vague → tone → proofread.

Disclosure: AI writing tools are third-party. No ranking, traffic, or earnings guarantees.

Table of contents

1. Nominalizations versus precise nouns
2. Why models default to noun-heavy lines
3. A search list for one editing session
4. Rewriting patterns without losing accuracy
5. Handoff with active voice and filler passes
6. Scenario: AdSense checklist section
7. Lines you should not flatten
8. FAQ
9. Run one nominalization sweep before tone

Nominalizations versus precise nouns

Not every noun ending in -tion is junk. Indexation in a GSC sentence may be the accurate term. Nominalization as a grammar label can stay in an AI-tools post. The target is empty abstraction that replaces a step.

Readers finish tasks faster when the first verb in a step is something they can click or type. Models prefer nouns because academic and policy corpora reward them.

One mistake beginners make is running active voice on sentences that still say “The enabling of caching is recommended”—you need to unpack enabling into “Turn on caching in…” first.

Why models default to noun-heavy lines

Completion models predict formal register tokens. Nominalizations increase perceived authority in training data without increasing information density. They also let the model skip naming you, Google, or the reader as subjects—which feels safer in generic articles but breaks how-to posts.

Mechanism for editors: each “conduct a review of” costs an extra clause before the reader learns what to open. On mobile, that delay pushes the first actionable line below the fold. Cutting nominalizations is a comprehension fix, not vanity grammar.

If I were editing a cluster post tonight, I would search tion, ment, ness, ity, and phrases like the process of—then judge each hit: precise term, quoted policy, or smokescreen.

A search list for one editing session

Work top to bottom once:

1. Highlight sentences with of the chains longer than three words (management of the configuration of).
2. Highlight perform / conduct / make + abstract noun (make a determination).
3. Highlight passive is required / is recommended without naming who requires it.
4. Rewrite to Subject + verb + object; keep links and numbers green-lit from your fact pass.

Optional: read only bold or list lead-ins. If two steps start with “Ensuring the proper…”, rewrite one to start with a verb.

This pass is narrower than tight edit, which merges paragraphs. It pairs with filler cut because filler often wraps nominalizations (“It is important to note the utilization of…”).

Rewriting patterns without losing accuracy

Examples of direction (adapt to your verified steps):

• “Implementation of the ads.txt file” → “Upload ads.txt to your site root (see Help).”
• “Consideration of search intent” → “Match the query intent before you draft.”
• “Optimization of internal linking” → “Add two internal links to the hub post.”

Do not invent steps while de-nomininalizing. If the draft had no real step under the noun stack, pull from your outline or delete the section—same rule as filler cutting.

For voice warmth, run tone rewrite after nominalizations so you are not polishing bureaucratic nouns you plan to delete.

Handoff with active voice and filler passes

Suggested order for CashPilot-style guides:

1. Paragraph structure and facts.
2. Filler templates.
3. Nominalizations (this page).
4. Active voice on remaining passives.
5. Cliché removal where stock metaphors remain.
6. Tone, then proofread.

The humanizing workflow owner explains how passes stack for affiliate and SEO posts. Nominalization work is the pass that stops tutorials from reading like RFP responses.

Scenario: AdSense checklist section

Before:

“Verification of site ownership through the utilization of the provided meta tag facilitates the commencement of review.”

After (facts unchanged, actors named):

“Paste the meta tag Google gives you in <head>, save the template, then click Verify in AdSense.”

You removed five abstract nouns and surfaced UI verbs the reader can execute. You did not add fake experience—only clearer grammar roles.

Lines you should not flatten

Keep official product nouns when quoting Help. Keep disclosure sentences even if they sound formal. Keep defined terms your audience searches (canonical tag, Page RPM) instead of vague verbs (fix the tag thing).

When unsure, compare to the vendor doc. If Google writes “Invalid traffic investigation,” mirror their noun in a quote; paraphrase the action in the next sentence with a verb.

FAQ

Schema-ready answers live in frontmatter; this section marks the pass boundary for editors sharing one checklist.

Run one nominalization sweep before tone

Nominalization cutting is not snobbery—it restores action visibility after AI drafts. Pair it with filler, active voice, and the full humanizing guide so each pass does one job. One sweep tonight on your messiest how-to section usually beats another full regenerate from the model.