If you've built an AI content workflow that technically works but produces copy you'd never actually publish, you've probably been troubleshooting your prompts. Makes sense. It wasn't even two years ago that the prevailing wisdom was your output quality hinges largely on how you word your prompts.
But the real problem is usually everything around the prompt — the context, meaning everything the model doesn't know about your brand, your audience, and your editorial worldview. What you need is context engineering, the discipline of building the right information environment for every step of your AI workflow.
Context Is the New Prompt
If prompt engineering is about crafting the right question, context engineering is about building the right information environment: the strategy, data, examples, constraints, and brand knowledge that shape every step of your workflow.
The term is recent but spread quickly. Shopify CEO Tobi Lutke coined it in June 2025, describing it as "the art of providing all the context for the task to be plausibly solvable by the LLM."
By late 2025, Anthropic had published a comprehensive engineering guide on it, and LangChain's State of Agent Engineering report found that 32% of organizations cite output quality as their top AI barrier with most failures traced to context, not model capability.
The 7 Elements of Great Context
The better AI content teams already do basic context engineering: SEO research, expert interviews as input, and reference examples to guide the models. Their output is grammatically correct, topically relevant, and factually sound.
It's also indistinguishable from all their competitors.
It’s garbage context in, garbage content out. Decent context produces decent content. Great content requires well-engineered context. These seven elements are the differentiating layer:
Brand intelligence: The brand's purpose, positioning, and business goals — but also the voice built from systematic analysis of best-performing content: vocabulary, rhetorical patterns, what makes it distinctive.
Author profile: The bylined person's background, expertise, and worldview — their biographical context, the opinions they hold, the analogies they reach for, how they actually think and communicate.
Strategic context: The content strategy and business goals behind it. What outcomes you're driving, where the brand aligns with industry consensus, where it deliberately diverges, and why.
Audience model: Not just demographics. What this audience already knows, what objections they carry, what they're trying to decide, and how they actually talk.
Competitive intelligence: What everyone else is already saying, so the model can take positions that are actually differentiated.
Quality examples: Exemplary pieces that demonstrate the target standard, not abstract rules about good writing.
Dynamic data: Recent engagement metrics, trending topics, your own recently published content so the AI isn't retreading last week's angles.
So why doesn't everyone do this? Because context engineering is genuinely hard.
It's Harder Than Most Teams Expect
Everyone hears "context engineering" and puts all the weight on "context." But “engineering” is half the work. This is essentially a system design challenge with four interlocking problems.
1. Curation
Out of everything that could be relevant, what does the model actually need for this specific step? You can’t just dump an entire knowledge base into a context window.
Chroma’s research on "context rot" shows that model accuracy degrades as irrelevant tokens accumulate, even well within technical limits — a finding Anthropic has built into their context engineering framework.
An 8-component brand kit might total 15,000 tokens, but a LinkedIn drafting step doesn't need the competitive intelligence section, and a brief-generation step doesn't need the format examples. Each step gets only what's relevant.
2. Freshness
Context goes stale faster than you'd expect. A competitor launches a campaign. A CEO says something on a podcast. Pricing changes overnight.
You need mechanisms to keep context current without someone manually updating a document every time something shifts. We pull engagement data from a social analytics API every 24 hours so our workflows know what's resonating. A separate tool monitors trending topics in each customer's industry — when it detects a signal, our system can draft a timely post within 30 to 60 minutes.
3. Orchestration
Different workflow steps need different context, from different sources, in different formats.
How do you get the latest search visibility data right before a production run? How do you make sure the brief-generation step has brand strategy but the drafting step has the voice guide? The challenge is designing data flows that deliver the right context at the right moment without human intervention.
4. Evaluation
How do you know your context is actually helping? A change to the voice guide might improve tone but degrade accuracy. We use two approaches. Blind tests — having stakeholders judge output without knowing whether it came from a different model, context configuration, or prompt — give you a quick directional signal. Ten to twenty rounds reveals what's actually working versus what you assume is working. For deeper refinement, structured evals are more powerful. Hamel Husain and Shreya Shankar's work shaped our approach here: treat evals as living product requirements, built from manual error analysis and then automated to track improvement over time.
And It's a Whole-Team Discipline
Two years of building AI content systems has taught us that this isn't one person's job, and you won't solve it with a single hire.
Doing it well requires four capabilities that rarely live in the same person:
Domain expertise: a deep knowledge of your industry, your customers' problems, and the language they use to describe them.
Editorial judgment: the ability to identify exemplary content, define voice, and calibrate quality.
Systems thinking: a feel for how context flows through a workflow, where feedback loops form, and how changes in one step ripple through the rest.
AI obsession: a compulsion to understand how models process context, where attention degrades, and how formatting affects output only comes from hands-on time with LLMs.
We ran into a problem with a customer's LinkedIn program: their posts kept retreading the same angles and structures.
We broke the pattern by adding engagement metrics so the AI could see what had already performed and avoid repeating it. That fix required editorial judgment to identify the problem, AI literacy to understand why it was happening, and systems thinking to solve it. No single person on either side of that engagement had all three.
When we work with customers, they bring domain expertise; we bring editorial judgment, systems thinking, and AI literacy. Neither side can do it alone. And as workflows mature, a fifth challenge emerges: the context library itself becomes an organizational system, managing the growing body of intelligence that everything else depends on. Without someone owning that, it accumulates as noise instead of compounding as an asset.
This is what we described in our AI Onion framework: an AI workflow touches everything around it: input collection, quality controls, feedback loops, organizational processes.
Here’s How to Start
Here's what we've learned from two years of building and iterating:
Get the basics right — and take them seriously. An up-to-date style guide, a curated set of gold standard examples, a clear audience profile, and a rich author bio. These four elements will improve any AI content workflow.
The author bio especially. A detailed, specific bio that captures how someone thinks, what analogies they reach for, and what opinions they hold produces noticeably more human output than a generic credential summary.
Don't overcomplicate your infrastructure. You'll probably outgrow your first tool in 18 months, and that's fine. AI makes migration easier than ever. A Notion database is a perfectly good starting point. Don't let infrastructure anxiety keep you from starting.
Make it easy for everyone to contribute context. Our services team didn't feel comfortable editing brand kits in AirOps, so we moved that to Notion. We built a Chrome extension, a Google Docs reader, and a YouTube transcription form — multiple input methods so people use whatever's comfortable. The best context system is one people actually feed.
Give your workflows awareness of recent content. Individual AI workflows have no memory of what you published last week. Without that awareness, you get repetition.
Inspect what your prompts actually contain. In a workflow tool, prompts look clean and manageable with variables. But when those variables resolve, the actual prompt sent to the model can be enormous, with significant overlap between sections.
Look at your resolved prompts regularly. You'll almost always find opportunities to compress, deduplicate, and improve.
Context Is Your Competitive Advantage
The next phase of AI content won't be defined by which companies have the best models — the distance between models narrows with every release. The defining advantage will be the systems companies build around those models, and context is the most critical layer.
Every improvement to your brand intelligence, every refined example, every better-structured knowledge base makes every subsequent output better. This compounds in a way that model access doesn't. A competitor can switch to the same model you're using overnight. They can't replicate two years of curated context.
And content is just where this starts. Every function that touches AI — research, operations, support — will face the same question: who owns the context?