What Is Content Engineering, and How Do You Do It?

A content writer creates a blog post. A content strategist decides which topics to cover. A content engineer designs the systems that produce content and make it discoverable by humans and AI.

In this post, I’ll cover what content engineering actually is, its core components, whose responsibility it is, and how you can become a fully-fledged Content Engineer.

Ryan Law’s 23 stage content pipeline built in Claude and polished in Agent A

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Skill and prompt engineering

Prompts are one-off instructions you give a model for a single task.

Skills are reusable, packaged instructions (often with examples or reference files) that a model can call on whenever a recurring task comes up.

Skills and prompts are how the pipeline knows what to do at each stage.

A drafting skill captures how a good article opens and closes, a citation skill captures the team’s sourcing standards, a formatting skill captures the shortcodes the CMS expects.

With skills and prompts, editorial decisions made once by a senior writer become available to the whole team every time the pipeline runs.

Knowledge and source-of-truth management

Pipelines break down without the right information feeding them.

Knowledge and source of truth (SoT) is the unglamorous foundation everything else rests on: making sure brand guidelines, product details, proprietary research, and SME interviews are structured and connected.

Without this, AI fills the gaps with generic language and information.

Mateusz’s Source of Truth knowledgebase built in Agent A

Orchestration and governance

Orchestration is the scheduling and triggering that turns a pipeline you manually start into one that runs itself.

Daily refresh jobs, weekly reports, event-triggered workflows.

Governance is the rules that stop it shipping bad work through fact-checking, citation verification, brand-voice enforcement, and human-review checkpoints.

What are the responsibilities of a content engineer day-to-day? 

A content engineer is responsible for building and maintaining the AI-powered systems a content team uses to produce, optimize, and distribute work at scale.

Using Agent A, we analyzed 20 US “Content Engineer” and “AI Content Engineer” job descriptions posted in 2025–2026 to see what the role actually consists of.

The defining responsibility is building an AI-augmented content pipeline (85%)—more universal than writing itself—followed by SEO/AEO/GEO (70%) and prompt engineering (65%).

In other words, the Content Engineer is a systems builder who happens to write, not a writer who happens to use AI.

Here’s a closer look at what content engineers actually do, and how they support the rest of the team.

Content production

Most content teams hit a ceiling on what they can produce manually. Content production engineering raises it.

Content production engineers help teams…

Ship faster

A content production engineer builds pipelines that map each stage from research to measurement, wired together in tools like Ahrefs’ Agent A or n8n. Nobody has to start their content from scratch.

Produce on-brand output every time

They build reusable skills, prompts, and custom instructions that codify the team’s collective know-how. The whole team can call on the same voice, structure, and editorial standards.

Draw on the company’s full knowledge

They build internal knowledge bases, Source of Truth (SoT), and RAG systems loaded with brand guidelines, product docs, ICPs, positioning frameworks, proprietary research, and SME interviews. The pipeline draws on the company’s full knowledge instead of generic language from training data.

Content maintenance

Content engineering also involves maintenance, which is the work of keeping published content performing over time.

Content maintenance engineers help teams…

Stay visible across search and AI surfaces

A content maintenance engineer builds automated SEO pipelines that set rules on structure, schema, metadata, and internal linking at the template level rather than page-by-page, and schedules refresh cycles so content stays current and visible on every surface; including search and AI.

Learn from what they ship

They build performance dashboards and feedback loops that pull traffic and AI visibility data from Google Search Console, GA, and Ahrefs Brand Radar into one weekly view. That data drives what gets retired or written next.

Catch decay before it hurts rankings

They build decay monitoring and refresh triggers that flag pages losing rankings, traffic, or AI citations, and queue them for an update. Whether that’s injecting fresher stats, new examples, or additional internal links.

Content distribution

Most content gets published, indexed, then forgotten. Content distribution engineering means the same source material can power a dozen touchpoints.

Content distribution engineers help teams…

Tailor content to different audiences

A content distribution engineer builds personalization and segmentation workflows that fork a single source piece into versions catered to different industries, roles, or lifecycle stages. For instance, that looks like local examples and tailored CTAs swapped in automatically.

Activate content beyond marketing

They build internal enablement pipelines that route published content into the systems other teams use: sales decks, battlecards, onboarding emails, support macros. Content stops dying at publish.

Reach readers through owned channels

They build email and lifecycle orchestration workflows that drop relevant content into newsletters, drip campaigns, and re-engagement sequences automatically, based on what’s been published and what each recipient has already read.

Claude Code, connect the Ahrefs MCP, and create a folder called content-pipeline. Inside it, create .claude/skills/ (where each skill lives) and six subfolders numbered 1-research/ through 6-performance/ (where each stage’s output goes).

Or log in to Agent A, where Claude and the Ahrefs connectors are already set up. Just ask it to create the folder structure for you.

Each skill reads from the previous folder and writes to the next.

This file structure is the thing that turns a collection of skills into a system. Without it, you’ll forget which version of which output came from which run.

Stage 1: Research

This skill takes a keyword and produces a markdown file with keyword volume, difficulty, parent topic, the top 10 SERP results, and the questions report, all pulled live from an SEO MCP (e.g. Ahrefs MCP).

The skill itself is just natural language instructions in plain markdown.

When called with a keyword, it tells the AI assistant to query the MCP for these specific reports, format the response as a markdown file with one section per data type, and save to the right folder.

Stage 2: Outlining

This skill reads the research file from stage one and produces an H2/H3 outline with target word counts per section and one-line notes on what each section should cover.

This is where your house style gets encoded; header patterns, section lengths, whether you lead with the answer (BLUF) or build to it.

Editorial decisions a senior writer would normally make on every piece get made once here, and applied automatically every time.

Stage 3: Drafting

This skill reads the outline and produces a full first draft. The draft skill should reference an /examples/ folder containing two or three of your best-published articles.

Without this, the output defaults to generic AI-speak.

With it, the system picks up your voice, including your sentence rhythm, paragraph length, and any small stylistic choices that make writing feel like yours.

Stage 4: Verification

This skill scans the draft for unsourced claims and either cites them or flags them.

It should look for stats, dates, named studies, and quoted figures, then searches for primary sources for each one.

Found a source? Inline link added.

No source? Claim flagged with [UNVERIFIED] so a human can decide.

This stage is what stops hallucinations reaching publication.

Stage 5: Formatting

This skill applies your CMS’s structural requirements to the verified draft, and outputs a CMS-ready version with shortcodes, schema, and internal links applied.

The further this stage goes, the less manual cleanup happens after publication.

Agent A’s WordPress connector can push the formatted output straight to a draft post if you’d rather skip the copy-paste.

Stage 6: Measurement

This skill runs monthly on each published piece.

You can build it to pull traffic, ranking, and AI citation data from Search Console, GA, and Ahrefs Brand Radar, and flag decaying pieces for refresh.

This is what makes the system learn.

What worked in cycle one informs cycle two; cycle two shapes cycle three, and so on.

After a few iterations, you’ll have a pipeline producing drafts you’d actually publish.

Kieran Flanagan puts it, the goal is to find one “Claude Code-pilled builder” who packages the team’s best workflows as skills and lets everyone else connect to what they build.

Kieran Flanagan: How to build marketing systems in Claude Code

2. Start with one specific bottleneck

Pick one expensive, repetitive process to fix first—e.g. refreshing decaying content or producing pages programmatically.

If you use Ahrefs, you’ve got a head start.

The diagnostic work already lives in your dashboard, and now in Agent A.

For instance, the app below is a ready-to-use Blog Freshness app that was built to flag decaying content.

3. Give them somewhere to build

Claude Code is one route.

Another is Agent A, which runs the same kind of multi-step workflows in the cloud—with deeper Ahrefs access, built-in skills, and the ability to share workflows.

Give your head content engineer the tools, data, and ability to fork their projects rather than have the whole team build from scratch and duplicate workload.

4. Measure what time they free up, not what they ship

It’s tempting to judge a content engineer on output volume.

But a better metric is time reclaimed for the rest of the team, whether that’s fewer hours on briefing, fact-checking, refreshing.

Report on what gets done with that freed-up capacity. If your writers are doing fewer rewrites and more original thinking, the role’s working.

SQ built a skill for exactly this: whenever a new Ahrefs blog post publishes, he runs /linkedin-pipeline on the URL in Agent A and generates three to five LinkedIn posts off the back of it.

They all adhere to his voice rules, fold-line placement, and hook patterns laid out in his skill files.

Engineering this kind of content is the easiest win there is: the pipeline produces it in your voice and the team stops spending creative energy on work that doesn’t need it.

Informational content

How-tos, definitions, explainers, and comparisons are the obvious fit for automated content.

They have predictable shapes a system can templatize, facts it can check.

They answer the kinds of queries AI assistants most commonly get asked.

Economically, they make sense too. Creating informational content from scratch has diminishing returns now that more than 58% of clicks are being eaten by AI.

Ryan Law turned the Ahrefs informational content process into code: 23 skills in Claude Code (and now Agent A), one for each stage of how a blog gets made, plus a master skill that runs them end-to-end.

A keyword goes in, a near-finished draft comes out; usually inside ten minutes.

Each skill outputs its own file, so any step can be reviewed or re-run without restarting.

Topics you already know inside out

When you know a topic well enough, the system drafts and you edit—your expertise is what stops bad output reaching the page.

But engineer content on subjects you’re unfamiliar with, and you’re putting a lot of faith in AI being right about things you can’t verify, and that’s how bad content works its way onto your site.

Even when it doesn’t, you end up doing all the fact-checking retrospectively, which just defeats the whole point. Any time you save on drafting gets added on at the other end.

“Experience matters: AI content is not, by default, good. This process works well because it mirrors our existing human editorial process, built from decades of collective content marketing experience.’ ” —How I Do Content Engineering with Claude Code, Ryan Law, Director of Content at Ahrefs

Content that uses proprietary data

Systems built around a company’s internal data—customer interviews, sales call transcripts, product analytics, support tickets—produce content nobody else can, even on topics that don’t yet have a body of public writing.

This is one of the most defensible forms of content engineering because the moat is the data, not the workflow.

Here’s a great example of this from Tiffany Kroll, Director of Growth at Prerender

“What I’m building—Athena—is GTM intelligence that learns from every customer conversation. Sales calls, CS calls, user interviews, podcasts, plus our product usage data and eventually external signals like competitor activity and category momentum. It watches patterns: when language shifts, when objections start trending, when sales is hearing one thing but marketing is writing about something else. Right now we run 30+ calls a month and nobody mines them. Sales hears one thing, marketing writes another. Athena closes that loop.”

Evergreen content with a long shelf life

AI systems are only as good as the material they have to work with.

For established topics, there’s decades of writing, research, and discussion the system can pull from.

I engineered a blog on content decay that took almost no time to edit and ship, and it’s performing pretty well organically.

It worked because the principles of content decay haven’t shifted much over the years, so the system had plenty of good material to draw on; and since it’s an evergreen topic, the blog won’t need a major rewrite any time soon.

A blog on the “best AI tools”, on the other hand, would need rewriting every few months to stay relevant.

The whole point of engineering content is that the work compounds.

You build the system once and it keeps producing.

If what it produces needs a constant rewrite, that undermines the whole value of the workflow.

Programmatic content

This is content built at scale from templates—location pages, currency conversion pages, app integrations, glossary entries.

It’s where content engineering pays back hardest, but also where it goes wrong most often.

The pages that work—Wise’s currency conversion pages, Zapier’s app pages, even our own Top Websites pages—succeed because they’re built on proprietary data the reader can actually use.

The pages that get penalized by Google are the ones filled with reshuffled SERP content dressed up as something new.

As Ryan Law puts it:

“Relevant, unique data is usually what makes the difference between helpful content and spam.”

If you’ve got the data to back it up, engineering content at this scale is exactly what the system is built for.

No data or original insights? You’re just scaling spam.

We threw our hat into the ring with our own programmatic content. Although traffic has dropped off, we still get 4.5M more visits based on the strength of our proprietary data.

Content that updates itself

Auto-detecting when stats are out of date, when linked sources have moved, when ranking has slipped, when a competitor has published something newer.

This is where the value of content engineering is in the maintenance.

Here’s a first-pass attempt at that.

I built The Blog Refresh Engine with Agent A.

It doesn’t automatically draft the content once it finds an update opportunity… yet.

But it does do some other pretty cool things.

For instance, it looks at an existing blog post, compares it against what’s currently ranking using Ahrefs’ AI Content Helper, and tells you which topics you’re missing or under-covering.

You get a list of accept/reject cards for suggested updates, and for the ones you accept, it drafts replacement paragraphs.

The drafts are the interesting part.

Instead of paraphrasing competitors (the usual failure mode of AI writing tools), it pulls from my own swipe file—every social media post, article, and video clip I’ve saved over the years—and uses those as raw material. 

Tools to use for content engineering 

If you take on a content engineering role, you’ve got two options.

You can start with a managed AI marketing agent that handles the infrastructure for you, or you can build the stack yourself.

Managed AI marketing agents

In this kind of environment, you build workflows by describing what you want the agent to do in natural language.

Ahrefs’ own version is Agent A, which comes with Ahrefs data access built in, so keyword research, SERP analysis, and AI-citation tracking are wired in already.

It’s the fastest way to start if your work centres on SEO and content data.

Your own DIY stack

If you’d rather build from individual parts for more control, deeper customization, or because your stack doesn’t centre on Ahrefs, here’s what you should add to your toolkit.

Knowledge base

This is where the raw material lives. Think brand guidelines, product docs, positioning frameworks, SME interviews. Obsidian, Notion, or Confluence all work; whichever your team already uses is usually the right answer.

Our Head of International Marketing, Erik Sarissky, has connected his Obsidian to Claude Code and built his own knowledge base

Using Obsidian with Claude Code, you can build an LLM Wiki; a structured, interlinked knowledge base Claude extracts ideas from and updates every time you add a new source. Over time, instead of forgetting what you’ve uploaded, it builds a persistent memory of your context that gets richer with every document.

AI coding environment

This is where you build the pipeline, encode the rules it follows (structure, metadata, formatting, citations), and chain skills together. Claude Code is the most common starting point; Cursor is the alternative if you’d rather work in an IDE.

Data sources

Your pipeline needs live context beyond your knowledge base. For instance, Ahrefs’ API and MCP plug in SEO and AI data, and Firehose handles anything that isn’t behind an API.

Analytics

The data your content generates shapes what you produce next.

Google Search Console and Google Analytics cover first-party traffic; Brand Radar covers AI search visibility.

Wiring these in creates the feedback loops that turn a static pipeline into one that improves with every run.

Workflow automation

Claude Code runs on your laptop, and the moment you close it, nothing happens.

As soon as you need scheduled jobs, webhooks, or anything running while you’re asleep, you need a server. n8n, Make, Gumloop, and Agent A all handle this natively in the cloud.

Image from Reddit thread: I developed an AI workflow that automatically responds to emails and saves 6h/week on Gumloop

Content management

Your pipeline has to publish somewhere.

Most teams already have WordPress, Webflow, Sanity, or Contentful in place.

The job is connecting the pipeline to the CMS’s API and pushing as much of the formatting work upstream as possible.

Version control

Git and GitHub become essential the moment you have more than a handful of skills or configs.

They let you branch, review, and roll back changes the same way developers do.

Final thoughts

If you engineer content well, you’ll spend less of your week making content, and more of it deciding what’s actually worth making.