A five-part framework for helping website content teams use AI to create stronger experiences and support growth.
Summary
Website content teams should use AI as part of a structured content process, not as a shortcut around one. Before asking ChatGPT, Claude, Copilot, Perplexity, or another AI tool to create content, teams need to give five layers of context: business, audience, strategy, sources, and execution.
Better context can improve AI-assisted work, but context alone doesn’t make the output good. Experienced content strategists, designers, writers, and subject-matter experts are still needed to recognize weak thinking, challenge generic output, spot missing nuance, and decide whether the work is actually useful, distinctive, and right for the customer.
Why isn’t prompt training enough?
A lot of AI training still starts with the prompt: how to structure one, which words to use, or how to build a prompt library for the team.
That can help. But a smart prompt won’t rescue weak context.
Imagine bringing a new content designer onto a major website redesign and asking them to write the homepage on day one. No business briefing. No audience research. No content strategy. No explanation of what customers need to understand, what’s already been decided, or which sources they can trust. Nobody would expect great work. But teams regularly expect exactly that from AI.
The better mindset is closer to onboarding a capable team member. Give the AI the material, explain the project, share the decisions already made, establish the rules, and then give it a task.
In AI circles, this broader idea is increasingly described as context engineering: deliberately determining what information, instructions, history, data, and other inputs a model needs to produce relevant results.
For content teams, the principle is simpler: build context before content.
What context does AI need for better website content?
At NewBrains, Max Abuyuan uses five layers of context to frame AI-assisted content work.
1. Business context: What are we actually trying to achieve?
Start with the reason the work exists. That might include a business goal, website goals, KPIs, the role of the experience, and what you need customers to do. This is the business brief.
Without this layer, AI can produce perfectly competent content that solves the wrong problem.
2. Audience context: Who are we trying to help?
Give AI the customer questions, needs, barriers, journey stage, and expected level of knowledge that should shape the experience.
Good website content is not just accurate. It helps someone make a decision.
That is also why a homepage, for example, should be organized around the questions customers need answered, not simply a familiar stack of website modules.
3. Strategic context: What have we already decided?
This includes content strategy, positioning, messaging, experience principles, information architecture, taxonomy, and other choices the team should not casually reinvent every time someone starts a new AI conversation. AI should work from the strategy, not create a new one.
The strategy becomes especially important on redesigns, where content needs should inform the experience before wireframes lock the team into assumptions.
4. Source context: What can AI trust?
Provide the research, analytics, interviews, product information, brand guidelines, approved claims, legal requirements, existing content, and other source material that should ground the work.
And be explicit about which sources take priority. Giving AI more information isn’t automatically better. Giving it the right information is.
A website content audit can be especially useful here because it helps distinguish what should be kept, combined, removed, or created before old website content becomes unquestioned AI source material.
5. Execution context: What are we making, and what does good look like?
Finally, define the page purpose, format, content model, SEO requirements, accessibility needs, voice, length, constraints, approval standards, and examples of strong work. So now the instruction has somewhere useful to land.
What does a strong AI-assisted content process actually look like?
A better workflow isn’t just about writing a better prompt. It is a sequence that gives AI enough information to work intelligently without asking it to invent the strategy.
1. Establish the role and objective.
Explain what the AI is helping with, what the website or page needs to achieve, and what success looks like.
2. Provide the project context.
Share the business background, audience needs, content strategy, information architecture, messaging decisions, prior work, and anything else the AI should understand before responding.
3. Load the source material.
Give it the research, analytics, product information, stakeholder input, brand guidance, approved claims, legal requirements, existing content, and other evidence it should rely on. Be clear about which sources are authoritative and which are reference material.
4. Tell AI how the work needs to work.
Explain the format, tone, content model, SEO requirements, accessibility needs, constraints, things to avoid, approval standards, and how the work will be evaluated.
5. Ask AI to identify gaps before it starts.
Have it flag missing information, contradictions, or assumptions that could change the direction or quality of the work. If the answers still live in someone’s head, let the AI interview the content lead, strategist, SME, or stakeholder.
6. Give it one clear task.
Once the context is strong, ask it to do something specific: analyze, structure, critique, generate options, draft, refine, or compare.
7. Put expert judgment over the work.
AI can check whether an output follows instructions, but that isn’t the same as knowing whether the strategy is smart, the insight is meaningful, the language is distinctive, or the experience will actually work for customers. A content strategist, designer, writer, SME, or other qualified expert still needs to evaluate the work using knowledge, experience, taste, and judgment.
AI can help create and critique the work. It can’t be the final judge of its own work!
What prompts can website content teams actually use?
These aren’t magic formulas. They are practical starting points for building a better AI-assisted workflow.
Prompt 1: Set up the AI working context
I’m going to brief you on a website content project before asking you to complete a task.
First, understand the project context. I’ll provide:
- the business and website objective
- the priority audience and customer needs
- the content and experience strategy
- information architecture, messaging, and decisions already made
- research and source material
- brand, legal, SEO, accessibility, and other requirements
- examples or prior work that should guide the output
- the format, constraints, and criteria for evaluating the work
Treat the materials I identify as sources it can trust unless I tell you otherwise.
Do not create the deliverable yet.
First:
- Summarize your understanding of the objective and the most important constraints.
- Identify any contradictions, missing information, or assumptions that could change the answer.
- Ask only the questions you need answered before proceeding.
Once those gaps are resolved, wait for the specific task.
Prompt 2: Let AI interview the stakeholder
Before beginning the task, interview me as the project stakeholder.
Your goal is to understand what you still need to know to produce strong website content.
Ask focused questions about:
- the business objective
- the audience and customer decision
- what the page or experience needs to accomplish
- strategic decisions already made
- available evidence or source material
- constraints, risks, or approvals
- what good looks like
Do not ask questions that can already be answered from the project context.
Prioritize only the gaps that could materially change the work.
Prompt 3: Control the sources
Use the materials I have provided as the source of truth for factual claims.
Distinguish clearly between:
- information supported by the provided sources
- strategic recommendations or interpretations
- information that is missing or needs verification
Do not fill factual gaps with assumptions. Flag them.
If sources conflict, identify the conflict before proceeding and explain which source you recommend prioritizing and why.
Prompt 4: Give AI the task after the context is established
Using the project context, sources, requirements, and decisions already provided, complete the following task:
Task: [insert one specific task]
Before answering, check that your output:
- supports the stated business and website objective
- addresses the priority audience and intended customer decision
- follows the agreed content strategy and messaging direction
- uses the provided source material accurately
- follows the required format and constraints
If the task requires information that has not been provided, flag the gap rather than inventing it.
Prompt 5: Review the work against the strategy
Review this content against the full project context rather than reviewing it as a standalone piece of writing.
Identify:
- anything inconsistent with the business objective or content strategy
- customer questions that remain unanswered
- unsupported claims or weak evidence
- generic language that could belong to a competitor
- unnecessary repetition
- places where hierarchy or structure makes the content harder to understand
- anything that could make the intended customer decision harder
Do not rewrite it yet.
First, explain the most important problems and rank them by impact.
The important part is not memorizing the prompts. It is the behaviour behind them:
brief, ground, question, work, critique.
Can good context prevent AI slop?
AI can follow every instruction in a brief and still produce something generic. It can combine accurate facts into a weak argument. It can create polished copy that sounds competent but says nothing distinctive. It can satisfy the format and still miss the customer.
That’s where human expertise matters. An experienced content strategist can see when the hierarchy is technically logical but strategically wrong. A strong writer can hear when the brand voice has flattened into generic AI polish. A subject-matter expert can catch a claim that sounds plausible but misses an important distinction.
Context gives AI better inputs. Human expertise determines whether the output deserves to exist.
What does this look like on a website redesign?
Consider a homepage. Instead of asking AI to “write a homepage for a financial services company,” the team can give it the role of the website, priority audiences, customer questions, positioning, IA, research, approved proof points, brand guidance, SEO intent, page requirements, legal constraints, and decisions already made. With that context, AI has a much better chance of producing work that supports the experience rather than simply filling a page.
AI can then help the team explore hierarchy, identify missing information, challenge assumptions, develop content directions, critique drafts, compare options, and accelerate execution. The team is still responsible for deciding what the homepage needs to communicate and how it should guide customers.
It also takes an experienced practitioner or expert to recognize that a polished headline says nothing distinctive, that an AI-recommended proof point is not persuasive enough, that the hierarchy overweights what the company wants to say, or that a page technically answers a customer question without answering it particularly well.
The AI didn’t replace the content strategy. The content strategy made the AI useful.
That distinction matters because website growth doesn’t come from producing more words faster. It comes from helping customers understand what matters, build confidence, and make better decisions.
Why should leaders care about shared AI context?
If everyone on a content team works from different assumptions, sources, terminology, and standards, AI can amplify that inconsistency very quickly. Shared context gives teams a common starting point, reducing repeated briefing, preventing people from reopening settled decisions, improving consistency, and making strong content processes more efficient.
That is where the growth opportunity gets more interesting. Better context can lead to better content, making it easier for customers to understand, compare, trust, and act. A shared process can also help teams get there with less unnecessary rework.
The goal isn’t to make content effortless or hand strategy over to AI. Humans still own the strategy, prioritization, judgment, customer empathy, taste, verification, risk, and final decision.
AI can accelerate parts of the work, show you options, challenge assumptions, and reduce some of the repetitive effort. But it doesn’t replace the expertise required to know what matters, what is true, what is distinctive, and what is good.
The opportunity is not to replace content experts. It is to give good content experts a better working system.
Sources
- Anthropic, Effective context engineering for AI agents
- Google Cloud, What is AI context engineering?
- Microsoft Azure Architecture Center, AI Technology Overview: Context engineering
- NewBrains, proprietary website strategy and content frameworks
