A content material author creates a weblog publish. A content material strategist decides which subjects to cowl. A content material engineer designs the techniques that produce content material and make it discoverable by people and AI.
On this publish, I’ll cowl what content material engineering truly is, its core elements, whose duty it’s, and how one can turn into a fully-fledged Content material Engineer.
Content material engineering is the apply of constructing the techniques that create content material, somewhat than producing content material piece by piece.
These techniques maintain the work that used to sit down on a author’s plate:
- Researching subjects
- Drafting and enhancing
- Optimizing for search and AI surfaces
- Publishing to a CMS
- Measuring what performs
A content material engineer designs the pipeline that strings these steps collectively utilizing AI, so their group can publish extra, sooner, and with out dropping model consistency or high quality.
There are two sorts of content material engineer
The time period “content material engineer” will get utilized in two other ways:
The structured content material engineer
This engineer designs taxonomies and metadata schemas so giant organizations can publish constant content material throughout channels, merchandise, and languages. Suppose Dell’s help docs or IKEA’s product data.
The AI pipeline content material engineer
The AI pipeline content material engineer automates the creation and optimization of content material so it may be discovered by search engine crawlers, AI bots, brokers, and no matter comes subsequent.
This text is in regards to the second sort.
4 overlapping practices outline content material engineering, and most engineers are juggling all of them at as soon as.
Pipeline design
Pipeline design means breaking the editorial course of into discrete, automatable steps.
For instance, a content material distribution pipeline may route a broadcast article via 5 phases: extracting key factors, producing format-specific variants, adapting every to a particular platform, scheduling publish occasions, and logging efficiency again to a dashboard.
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Talent and immediate engineering
Prompts are one-off directions you give a mannequin for a single process.
Expertise are reusable, packaged directions (usually with examples or reference recordsdata) {that a} mannequin can name on at any time when a recurring process comes up.
Expertise and prompts are how the pipeline is aware of what to do at every stage.
A drafting talent captures how a very good article opens and closes, a quotation talent captures the group’s sourcing requirements, a formatting talent captures the shortcodes the CMS expects.
With expertise and prompts, editorial choices made as soon as by a senior author turn into accessible to the entire group each time the pipeline runs.
Data and source-of-truth administration
Pipelines break down with out the suitable data feeding them.
Data and supply of fact (SoT) is the unglamorous basis all the pieces else rests on: ensuring model pointers, product particulars, proprietary analysis, and SME interviews are structured and related.
With out this, AI fills the gaps with generic language and data.
Mateusz’s Supply of Reality knowledgebase in-built Agent A
Orchestration and governance
Orchestration is the scheduling and triggering that turns a pipeline you manually begin into one which runs itself.
Every day refresh jobs, weekly experiences, event-triggered workflows.
Governance is the foundations that cease it transport unhealthy work via fact-checking, quotation verification, brand-voice enforcement, and human-review checkpoints.
A content material engineer is accountable for constructing and sustaining the AI-powered techniques a content material group makes use of to provide, optimize, and distribute work at scale.
Utilizing Agent A, we analyzed 20 US “Content material Engineer” and “AI Content material Engineer” job descriptions posted in 2025–2026 to see what the function truly consists of.
The defining duty is constructing an AI-augmented content material pipeline (85%)—extra common than writing itself—adopted by search engine optimisation/AEO/GEO (70%) and immediate engineering (65%).
In different phrases, the Content material Engineer is a techniques builder who occurs to jot down, not a author who occurs to make use of AI.
Right here’s a more in-depth take a look at what content material engineers truly do, and the way they help the remainder of the group.
Content material manufacturing
Most content material groups hit a ceiling on what they’ll produce manually. Content material manufacturing engineering raises it.
Content material manufacturing engineers assist groups…
Ship sooner
A content material manufacturing engineer builds pipelines that map every stage from analysis to measurement, wired collectively in instruments like Ahrefs’ Agent A or n8n. No person has to start out their content material from scratch.
Produce on-brand output each time
They construct reusable expertise, prompts, and customized directions that codify the group’s collective know-how. The entire group can name on the identical voice, construction, and editorial requirements.
Draw on the corporate’s full information
They construct inside information bases, Supply of Reality (SoT), and RAG techniques loaded with model pointers, product docs, ICPs, positioning frameworks, proprietary analysis, and SME interviews. The pipeline attracts on the corporate’s full information as a substitute of generic language from coaching information.
Content material upkeep
Content material engineering additionally entails upkeep, which is the work of holding printed content material performing over time.
Content material upkeep engineers assist groups…
Keep seen throughout search and AI surfaces
A content material upkeep engineer builds automated search engine optimisation pipelines that set guidelines on construction, schema, metadata, and inside linking on the template stage somewhat than page-by-page, and schedules refresh cycles so content material stays present and visual on all surfaces; search and AI alike.
Be taught from what they ship
They construct efficiency dashboards and suggestions loops that pull visitors and AI visibility information from Google Search Console, GA, and Ahrefs Model Radar into one weekly view. That information drives what will get retired or written subsequent.
Catch decay earlier than it hurts rankings
They construct decay monitoring and refresh triggers that flag pages dropping rankings, visitors, or AI citations, and queue them for an replace. Whether or not that’s injecting brisker stats, new examples, or extra inside hyperlinks.
Content material distribution
Most content material will get printed, listed, then forgotten. Content material distribution engineering means the identical supply materials can energy a dozen touchpoints.
Content material distribution engineers assist groups…
Tailor content material to totally different audiences
A content material distribution engineer builds personalization and segmentation workflows that fork a single supply piece into variations catered to totally different industries, roles, or lifecycle phases. For example, that appears like native examples and tailor-made CTAs swapped in robotically.
Activate content material past advertising and marketing
They construct inside enablement pipelines that route printed content material into the techniques different groups use: gross sales decks, battlecards, onboarding emails, help macros. Content material stops dying at publish.
Attain readers via owned channels
They construct e-mail and lifecycle orchestration workflows that drop related content material into newsletters, drip campaigns, and re-engagement sequences robotically, primarily based on what’s been printed and what every recipient has already learn.
If you wish to construct somewhat than learn, right here’s the skeleton. Six expertise, one grasp talent.
You may get a V1 working in a day.
Each pipeline you’ll ever construct is a few variation of this one, simply with extra expertise bolted on.
Earlier than you begin
Set up Claude Code, join the Ahrefs MCP, and create a folder known as content-pipeline. Inside it, create .claude/expertise/ (the place every talent lives) and 6 subfolders numbered 1-research/ via 6-performance/ (the place every stage’s output goes).
Or log in to Agent A, the place Claude and the Ahrefs connectors are already arrange. Simply ask it to create the folder construction for you.
Every talent reads from the earlier folder and writes to the subsequent.
This file construction is the factor that turns a group of expertise right into a system. With out it, you’ll overlook which model of which output got here from which run.
Stage 1: Analysis
This talent takes a key phrase and produces a markdown file with key phrase quantity, problem, mother or father matter, the highest 10 SERP outcomes, and the questions report, all pulled stay from an search engine optimisation MCP (e.g. Ahrefs MCP).
The talent itself is simply pure language directions in plain markdown.
When known as with a key phrase, it tells the AI assistant to question the MCP for these particular experiences, format the response as a markdown file with one part per information sort, and save to the suitable folder.
Stage 2: Outlining
This talent reads the analysis file from stage one and produces an H2/H3 define with goal phrase counts per part and one-line notes on what every part ought to cowl.
That is the place your home model will get encoded; header patterns, part lengths, whether or not you lead with the reply (BLUF) or construct to it.
Editorial choices a senior author would usually make on each piece get made as soon as right here, and utilized robotically each time.
Stage 3: Drafting
This talent reads the define and produces a full first draft. The draft talent ought to reference an /examples/ folder containing two or three of your best-published articles.
With out this, the output defaults to generic AI-speak.
With it, the system picks up your voice, together with your sentence rhythm, paragraph size, and any small stylistic decisions that make writing really feel like yours.
Stage 4: Verification
This talent scans the draft for unsourced claims and both cites them or flags them.
It ought to search for stats, dates, named research, and quoted figures, then searches for main sources for every one.
Discovered a supply? Inline hyperlink added.
No supply? Declare flagged with [UNVERIFIED] so a human can resolve.
This stage is what stops hallucinations reaching publication.
Stage 5: Formatting
This talent applies your CMS’s structural necessities to the verified draft, and outputs a CMS-ready model with shortcodes, schema, and inside hyperlinks utilized.
The additional this stage goes, the much less guide cleanup occurs after publication.
Agent A’s WordPress connector can push the formatted output straight to a draft publish if you happen to’d somewhat skip the copy-paste.
Stage 6: Measurement
This talent runs month-to-month on every printed piece.
You’ll be able to construct it to tug visitors, rating, and AI quotation information from Search Console, GA, and Ahrefs Model Radar, and flag decaying items for refresh.
That is what makes the system study.
What labored in cycle one informs cycle two; cycle two shapes cycle three, and so on.
After just a few iterations, you’ll have a pipeline producing drafts you’d truly publish.
Listed here are 4 suggestions for constructing a group of content material engineers…
1. Appoint a head engineer, allow them to show the mannequin—then scale
You don’t want a complete content material engineering operate from day one.
It’s essential to spot the techniques thinker already on the group.
As Kieran Flanagan places it, the aim is to seek out one “Claude Code-pilled builder” who packages the group’s finest workflows as expertise and lets everybody else connect with what they construct.
2. Begin with one particular bottleneck
Decide one costly, repetitive course of to repair first—e.g. refreshing decaying content material or producing pages programmatically.
When you use Ahrefs, you’ve received a head begin.
The diagnostic work already lives in your dashboard, and now in Agent A.
For example, the app under is a ready-to-use Weblog Freshness app that was constructed to flag decaying content material.
3. Give them someplace to construct
Claude Code is one route.
One other is Agent A, which runs the identical type of multi-step workflows within the cloud—with deeper Ahrefs entry, built-in expertise, and the flexibility to share workflows.
Give your head content material engineer the instruments, information, and talent to fork their initiatives somewhat than have the entire group construct from scratch and duplicate workload.
4. Measure what time they unlock, not what they ship
It’s tempting to evaluate a content material engineer on output quantity.
However a greater metric is time reclaimed for the remainder of the group, whether or not that’s fewer hours on briefing, fact-checking, refreshing.
Report on what will get performed with that freed-up capability. In case your writers are doing fewer rewrites and extra authentic considering, the function’s working.
I’ve tried engineering all types of content material these days. Some one-click drafts are virtually ship-ready; others I wouldn’t rush to place my title to.
Generally that’s as a result of the pipeline wants fixing, however most frequently it’s as a result of the content material will not be the suitable match for engineering within the first place.
AI pipelines work finest when the construction is predictable, the information are checkable, or the author can truly decide whether or not the output is any good.
Right here’s how I’d categorize the sorts of content material which can be well worth the engineering effort.
Repetitive, repurposed, or templated content material
Some content material needs to be written, however doesn’t actually should be written.
The construction is similar each time, and the worth is within the data, not the prose round it.
I’m speaking: launch notes, weekly digests, recurring replace emails, changelog entries, and most repurposing work.
My colleague SQ constructed a talent for precisely this: at any time when a brand new Ahrefs weblog publish publishes, he runs /linkedin-pipeline on the URL in Agent A and generates three to 5 LinkedIn posts off the again of it.
All of them adhere to his voice guidelines, fold-line placement, and hook patterns specified by his talent recordsdata.
Engineering this sort of content material is the best win there may be: the pipeline produces it in your voice and the group stops spending artistic vitality on work that doesn’t want it.
Informational content material
How-tos, definitions, explainers, and comparisons are the plain match for automated content material.
They’ve predictable shapes a system can templatize, information it may well verify.
They reply the sorts of queries AI assistants mostly get requested.
Economically, they make sense too. Creating informational content material from scratch has diminishing returns now that greater than 58% of clicks are being eaten by AI.
Ryan Regulation turned the Ahrefs informational content material course of into code: 23 expertise in Claude Code (and now Agent A), one for every stage of how a weblog will get made, plus a grasp talent that runs them end-to-end.
A key phrase goes in, a near-finished draft comes out; often inside ten minutes.
Every talent outputs its personal file, so any step may be reviewed or re-run with out restarting.
Subjects you already know inside out
When a subject nicely sufficient, the system drafts and also you edit—your experience is what stops unhealthy output reaching the web page.
However engineer content material on topics you’re unfamiliar with, and also you’re placing quite a lot of religion in AI being proper about issues you’ll be able to’t confirm, and that’s how unhealthy content material works its manner onto your web site.
Even when it doesn’t, you find yourself doing all of the fact-checking retrospectively, which simply defeats the entire level. Any time you save on drafting will get added on on the different finish.
“Expertise issues: AI content material will not be, by default, good. This course of works nicely as a result of it mirrors our current human editorial course of, constructed from many years of collective content material advertising and marketing expertise.’ ” —How I Do Content material Engineering with Claude Code, Ryan Regulation, Director of Content material at Ahrefs
Content material that makes use of proprietary information
Programs constructed round an organization’s inside information—buyer interviews, gross sales name transcripts, product analytics, help tickets—produce content material no person else can, even on subjects that don’t but have a physique of public writing.
This is likely one of the most defensible types of content material engineering as a result of the moat is the information, not the workflow.
Right here’s an awesome instance of this from Tiffany Kroll, Director of Progress at Prerender
“What I’m constructing—Athena—is GTM intelligence that learns from each buyer dialog. Gross sales calls, CS calls, consumer interviews, podcasts, plus our product utilization information and finally exterior indicators like competitor exercise and class momentum. It watches patterns: when language shifts, when objections begin trending, when gross sales is listening to one factor however advertising and marketing is writing about one thing else. Proper now we run 30+ calls a month and no person mines them. Gross sales hears one factor, advertising and marketing writes one other. Athena closes that loop.”
Evergreen content material with an extended shelf life
AI techniques are solely nearly as good as the fabric they need to work with.
For established subjects, there’s many years of writing, analysis, and dialogue the system can pull from.
I engineered a weblog on content material decay that took virtually no time to edit and ship, and it’s performing fairly nicely organically.
It labored as a result of the ideas of content material decay haven’t shifted a lot over time, so the system had loads of good materials to attract on; and because it’s an evergreen matter, the weblog gained’t want a significant rewrite any time quickly.
A weblog on the “finest AI instruments”, however, would wish rewriting each few months to remain related.
The entire level of engineering content material is that the work compounds.
You construct the system as soon as and it retains producing.
If what it produces wants a relentless rewrite, that undermines the entire worth of the workflow.
Programmatic content material
That is content material constructed at scale from templates—location pages, forex conversion pages, app integrations, glossary entries.
It’s the place content material engineering pays again hardest, but in addition the place it goes fallacious most usually.
The pages that work—Sensible’s forex conversion pages, Zapier’s app pages, even our personal High Web sites pages—succeed as a result of they’re constructed on proprietary information the reader can truly use.
The pages that get penalized by Google are those crammed with reshuffled SERP content material dressed up as one thing new.
As Ryan Regulation places it:
“Related, distinctive information is often what makes the distinction between useful content material and spam.”
When you’ve received the information to again it up, engineering content material at this scale is strictly what the system is constructed for.
No information or authentic insights? You’re simply scaling spam.
We threw our hat into the ring with our personal programmatic content material. Though visitors has dropped off, we nonetheless get 4.5M extra visits primarily based on the power of our proprietary information.
Content material that updates itself
Auto-detecting when stats are outdated, when linked sources have moved, when rating has slipped, when a competitor has printed one thing newer.
That is the place the worth of content material engineering is within the upkeep.
Right here’s a first-pass try at that.
I constructed The Weblog Refresh Engine with Agent A.
It doesn’t robotically draft the content material as soon as it finds an replace alternative… but.
However it does do another fairly cool issues.
For example, it seems at an current weblog publish, compares it in opposition to what’s at the moment rating utilizing Ahrefs’ AI Content material Helper, and tells you which ones subjects you’re lacking or under-covering.
You get an inventory of settle for/reject playing cards for advised updates, and for those you settle for, it drafts substitute paragraphs.
The drafts are the attention-grabbing half.
As an alternative of paraphrasing opponents (the same old failure mode of AI writing instruments), it pulls from my very own swipe file—each social media publish, article, and video clip I’ve saved over time—and makes use of these as uncooked materials. 
When you tackle a content material engineering function, you’ve received two choices.
You can begin with a managed AI advertising and marketing agent that handles the infrastructure for you, or you’ll be able to construct the stack your self.
Managed AI advertising and marketing brokers
In this sort of setting, you construct workflows by describing what you need the agent to do in pure language.
Ahrefs’ personal model is Agent A, which comes with Ahrefs information entry in-built, so key phrase analysis, SERP evaluation, and AI-citation monitoring are wired in already.
It’s the quickest solution to begin in case your work centres on search engine optimisation and content material information.
Your individual DIY stack
When you’d somewhat construct from particular person components for extra management, deeper customization, or as a result of your stack doesn’t centre on Ahrefs, right here’s what it’s best to add to your toolkit.
Data base
That is the place the uncooked materials lives. Suppose model pointers, product docs, positioning frameworks, SME interviews. Obsidian, Notion, or Confluence all work; whichever your group already makes use of is often the suitable reply.
Our Head of Worldwide Advertising and marketing, Erik Sarissky, has related his Obsidian to Claude Code and constructed his personal information base
Utilizing Obsidian with Claude Code, you’ll be able to construct an LLM Wiki; a structured, interlinked information base Claude extracts concepts from and updates each time you add a brand new supply. Over time, as a substitute of forgetting what you’ve uploaded, it builds a persistent reminiscence of your context that will get richer with each doc.
AI coding setting
That is the place you construct the pipeline, encode the foundations it follows (construction, metadata, formatting, citations), and chain expertise collectively. Claude Code is the most typical start line; Cursor is the choice if you happen to’d somewhat work in an IDE.
Information sources
Your pipeline wants stay context past your information base. For example, Ahrefs’ API and MCP plug in search engine optimisation and AI information, and Firehose handles something that isn’t behind an API.
Analytics
The info your content material generates shapes what you produce subsequent.
Google Search Console and Google Analytics cowl first-party visitors; Model Radar covers AI search visibility.
Wiring these in creates the suggestions loops that flip a static pipeline into one which improves with each run.
Workflow automation
Claude Code runs in your laptop computer, and the second you shut it, nothing occurs.
As quickly as you want scheduled jobs, webhooks, or something working whilst you’re asleep, you want a server. n8n, Make, Gumloop, and Agent A all deal with this natively within the cloud.
Picture from Reddit thread: I developed an AI workflow that robotically responds to emails and saves 6h/week on Gumloop
Content material administration
Your pipeline has to publish someplace.
Most groups have already got WordPress, Webflow, Sanity, or Contentful in place.
The job is connecting the pipeline to the CMS’s API and pushing as a lot of the formatting work upstream as attainable.
Model management
Git and GitHub turn into important the second you might have greater than a handful of expertise or configs.
They allow you to department, overview, and roll again modifications the identical manner builders do.
Ultimate ideas
When you engineer content material nicely, you’ll spend much less of your week making content material, and extra of it deciding what’s truly price making.
