EXECUTION · Chapter 3
The AI content engine.
A six-stage workflow that turns a 10-minute brief into a published, schema-rich, AI-citable page in a focused 90-minute cycle. Designed for a marketer of one, built to scale to a team of five.
The six stages
- 1
Plan
Pillar topic, target query cluster, ICP question being answered, target word count, schema types, internal links to assign. A 10-minute brief drives a 90-minute production cycle.
- 2
Draft
AI produces the first pass against the brief, in the brand voice prompt. Output is a Markdown file with passage-shaped H2s, sourced citations, and FAQ pairs already structured.
- 3
Edit
A human runs three passes: factual (does every claim have a source), voice (does it sound like the brand or like the median competitor), and structural (are passages standalone-extractable for AI citation).
- 4
Enrich
Schema (Article, FAQ, BreadcrumbList), OG image, alt text, internal links to the hub and sister content, last-updated stamp. None of this is optional in 2026.
- 5
Publish
Commit, deploy, ping IndexNow for Bing, request indexing in GSC for high-priority pages. The first crawl window matters. IndexNow is the cheapest win.
- 6
Monitor
Two-week cycle: GSC for queries and impressions, prompt tests across ChatGPT, Perplexity, Claude, and Copilot for citations, refresh stamp where data shifts.
Where AI earns its seat, and where it doesn't
A scored view of how much of each stage to hand to a language model in 2026.
LLMs draft to spec when the brief is specific. Bad briefs make bad drafts. Fix the brief, not the model.
Mechanical and consistent. Validate with the Rich Results Test before publish.
One unique image per page. Optimize to WebP/AVIF before shipping.
The judgement is the asset. Outsource this to AI and your brand averages to the median competitor.
LLMs hallucinate citations. A human checks every external link and statistic before publish.
- Teams publishing one to four times per week with a single editor.
- Businesses with deep subject-matter expertise that is currently locked in the founder's head.
- Companies refreshing legacy content quarterly to stay AI-citable.
- There is no editor. AI drafts go straight to publish and the site drifts toward template prose.
- The brand voice has never been written down and every post sounds different.
- Volume is the goal, not authority. The engine rewards depth, not pages-per-day.
- Every published post has a sourced external citation, an internal link to the hub, and FAQ schema.
- Last-updated stamp is visible above the fold on every long-form page.
- AI-crawler allow rules are live in robots.txt and llms.txt before content production starts.
Frequently asked
How much of the AI content engine should be automated?
+
Draft generation, schema assembly, image generation, and IndexNow submission can be fully automated. Brief writing, editing, and the choice of which topics to publish must stay human. Companies that automate the editorial decisions ship volume without authority, and the drop in quality shows up in rankings within a few months.
Will AI-generated content hurt SEO?
+
Google's position since 2023 is that AI-generated content is fine if it demonstrates expertise, experience, authoritativeness, and trustworthiness, the same bar applied to human content. Pages that are obviously templated, source-free, or hallucinated get penalized. Pages with original data, citations, named author, and clear point of view rank as well as human-only content.
How fast should an SMB publish?
+
One high-quality, source-cited, schema-complete piece per week beats five thin posts per week. For most 5 to 50 person businesses, weekly is the realistic cadence when AI handles drafting and a human spends a focused hour editing, checking facts, and adding the details only someone in the business would know.
What is a passage-shaped H2?
+
A passage-shaped H2 is a question or noun phrase under which the next paragraph reads as a complete, self-contained answer, extractable without further context. Example: 'What is a passage-shaped H2?' followed by a decisive opening sentence. AI engines lift the answer beneath the question; vague subheads do not get extracted.
Do I need original images for every post?
+
You need a unique image per page. Reusing the same hero image across multiple posts is one of Google's textbook doorway-page signals and erodes trust on returning visitors. Generated imagery is fine if every page gets its own asset, optimized to WebP or AVIF and under a few hundred KB.
Who should write the first draft, a person or an AI model?
+
An AI model, working from a detailed brief a person wrote. The brief carries the facts, the angle, and any client-specific detail the model cannot know. Skipping the brief and asking a model to write from a bare topic produces the generic, source-free content that both readers and Google's quality systems can spot immediately.
How does the content engine handle fact-checking?
+
Every claim that cites a number, a statistic, or a third-party source needs a working link to that source before publish, checked by a person, not assumed correct because the model produced it. AI models can state a wrong number with the same confidence as a right one. The editing step exists specifically to catch that.
How does the content engine decide what topic to publish next?
+
From the content pillars set in the strategy chapter, cross-referenced against Google Search Console's queries-with-impressions data to find topics people already search for but the site does not yet answer well. Picking topics from a brainstorm instead of from real search demand is the most common way a content engine produces volume without traffic.
In the field
How an SMB runs this in practice
The chapter above is the reference. For a real-world account of how a one-person marketing function set up the engine, including the three failure modes to watch for. Read the companion post.
How an SMB Builds an AI Content Engine Without a Content Team →