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AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator. 

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm. 

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization. 

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it: 

• “If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.” 

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines: 

• What the review process for AI-created content looks like. 
• When and how to disclose AI involvement in content creation. 
• How to protect proprietary information (not uploading confidential or client information into AI tools).
• Which AI tools are approved for use, and how to request access to new ones.
• How to log or report problems.

Logically, the policy will evolve as the technology and regulations change. 

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well. 

LLMs are great at predicting the next best sentence and making it sound convincing. 

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value. 

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem. 

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

• “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” 

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

• Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
• Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
• Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources. 
• Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
• Create detailed author bios: Include:
  • Relevant qualifications, certifications, awards, and experience.
  • Links to social media, academic papers (if relevant), or other authoritative works.
• Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
• Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.

Dig deeper: Writing people-first content: A process and template

Train the LLM 

LLMs are trained on vast amounts of data – but they’re not trained on your data. 

Put in the work to train the LLM, and you can get better results and more efficient workflows. 

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

• Audience personas.
• Voice traits that matter.
• Reading level, if applicable.
• The do’s and don’ts of phrases and language to use. 
• Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc. 

You can refresh this as needed and use it to further train the model over time. 

Build a prompt kit  

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with: 

• The style guide
  • This covers everything from the audience personas to the voice style and formatting.
  • If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time. 
• A content brief template
  • This can be an editable document that’s filled in for each content project and includes things like:
    • The goal of the content.
    • The specific audience.
    • The style of the content (news, listicle, feature article, how-to).
    • The role (who the LLM is writing as).
    • The desired action or outcome.
• Content examples
  • Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more. 
  • If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic. 
• Sources
  • Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research. 
  • For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.  
  • As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
• SEO prompts
  • Consider building SEO into the structure of the content from the outset.  
  • Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

• Crafting a direct answer in the first one to two sentences, then expanding with context.
• Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
• Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
• Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
• Provide clear citations and semantic richness (synonyms, related entities) throughout. 

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG 

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules. 

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base. 

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement. 

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

• “Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.) 
• “Is the tone and voice completely aligned with the style guide?” 

Have an established editing process 

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.  

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality. 

This might include:

• Identifying the parts of the content creation workflow that are best suited for LLM assistance.
• Conducting an editorial meeting to sign off on topics and outlines. 
• Drafting the content.
• Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
• Getting sign-off from stakeholders.  

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

• Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
• All facts are traceable to credible, approved sources.
• Outdated statistics (more than two years) are replaced with fresh insights. 
• Draft meets the style guide’s voice guidelines and tone definitions. 
• Content adds valuable, expert insights rather than being vague or generic.
• For thought leadership, ensure the author’s perspective is woven throughout.
• Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI. 
• Draft aligns with brand values and meets internal publication standards.
• Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search. 

Dig deeper: An AI-assisted content process that outperforms human-only copy

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