Balancing Quantity and Quality in Content Engineering

We’ve all heard the phrase “we need more content” so many times it makes our ears bleed. Volume is always the play, and for good reason—the more present you are online, the more likely prospects are to find you. Now we’ve got AI to actually make that possible, scaling the skills we’ve spent years honing. And I’m pretty damn excited about it. I’m an AI fangirl through and through, and the chance to multiply myself digitally feels like magic.

But…I’m also a quality whore (yep, I said it). I don’t feel good publishing anything average, and I’ve never once hit “publish” on an article that came straight from AI. It just doesn’t work. Audiences can feel the burden of bad writing. Think about the first five results of almost any search—bland, basic, forgettable. That’s what happens when quality gets sacrificed for quantity.

Here’s the good news: you don’t have to choose. You can scale your volume and still protect quality. Let’s talk about how.

What makes content high quality

A lot of lists about “high quality content” talk about accuracy, originality, clarity, and structure. All of which are vital components of good content—but they’re also table stakes. In my experience, real quality goes further than that.

True high-quality content creates connection. It:

• makes your audience feel understood.

• gives them deep insights they couldn’t easily get elsewhere.

• helps them see familiar problems in a new light

• opens their eyes to options they hadn’t considered.

• acknowledges the people, processes, and tech they find frustrating, overwhelming, or exciting.

It’s innately human. It’s gritty, unscripted, sometimes off-color. Sometimes it means being vulnerable—and even making mistakes.

Take the example of Sweet Loren’s cookie dough TikTok mishap, the situation where they accidentally changed their account name to “Ryan.” The video went viral not because it was perfect, but because it felt real, funny, and unexpectedly delightful. That moment underscored something critical: authenticity outperforms polish every time.

Those are the true signals your content needs to send: I’m a real person, experiencing real things, and understanding what you’re dealing with.

And only humans can make sure this happens. AI won’t magically surface your customer’s lived reality, or the nuance of how one role talks differently from another without your direction. The best a content system can do is create space for that input and then translate it into natural language at scale.

That’s why a well-designed content engineering workflow isn’t just about efficiency. It’s about making sure your team spends more time on strategy, persona knowledge, and insights—because those are the ingredients of quality.

Strategies to maintain content quality along with quantity

Let’s look at a few ways you can leverage your human expertise to product high quality content at scale.

Spend quality time with your AI

The “how” of quality starts with the humans of quality. Yes, automation is intended to reduce human effort, but it should also maximize the places where human input is most valuable.

The way I see it is that a strong content engineering system should make more room for the things only you can provide—your knowledge of the audience, your brand’s perspective, your sense of what’s actually useful. Yes, automation frees up time, but that only matters if you’re feeding those insights back into the machine. And if the system is well-designed, it should encourage you to do this.

Think about the questions you can discuss with AI:

• What’s the biggest day-to-day challenge this audience is dealing with?

• What do they complain about that their boss never hears?

• How do we talk to them at their level, without jargon or fluff?

• What’s the one thing they care about most in this topic?

If your system is working right, you’ll have the time and mental space to dig into those answers and even have a conversation with your AI to tease out some of the finer points of your topic.

Prioritize conversations over prompts

Right now, “prompt engineering” gets a lot of hype. But honestly? Prompts are table stakes. They’re not enough to get the job done. What matters is how you think with the AI. It’s not about nailing the perfect single prompt—it’s about holding the right kind of conversation.

When I work with AI tools, the best results come from back-and-forth exchanges. I’ll ask a question, challenge the answer, refine it, push it deeper, and then pull in my own perspective. That’s where the magic happens. The system is built to function best from this kind of dialogue, not provide a single answer from a single input. Prompts alone won’t elevate your content. A well-directed conversation will.

That said, prompts are obviously part of an AI conversation so use them to force better thinking. Build them into workflows that nudge you and your team to ask hard questions:

• Does this draft clearly reflect our persona’s top challenges?

• Have we gone beyond surface-level advice?

• Are we backing up claims with examples, stats, or real scenarios?

• Would I, as the reader, find this genuinely helpful?

Questions like these act as a built-in editor. They keep you from taking shortcuts just because the system makes it easy.

Editorial Input isn’t optional

This is where a lot of companies miss the mark. They put a tech-focused content engineer in charge of automated workflows, not realizing that building pipelines isn’t the same as building strong narratives.

Engineering can streamline production, but editorial experience keeps the voice consistent and the story engaging. A good editor knows when something feels flat and how to sharpen it. Without that experience alongside the system, you end up with content that checks all the boxes but lands with no impact.

From what I’ve seen, most people engineering content flows right now are chasing speed. They hope quality will come along for the ride, but it usually doesn’t. That’s an early maturity issue. Everyone’s so excited about what AI can do that they can’t see how far they’re veering off course. I expect we’ll see a correction soon, when companies realize quality problems are undercutting their results. The ones who succeed will find a middle ground between the slowness of manual work and the raw speed of AI.

In practice, this means building “pauses” into the pipeline—short human-AI checkpoints at key moments where editorial can provide input: after the idea is surfaced, after the outline, after research, and after the first draft.

BONUS: Build a repository or checkpoint in the process where you can drop in SME or customer input to really deliver something unique and uncopyable to your audience.

Even spending five minutes at each of these stages makes a huge difference in quality. Here's what that workflow might look like:

Human feedback steps should feel like quick checkpoints, not extra work. Don’t make your team click through four menus just to get into an AI chat. As each stage completes, they should get a single notification in the platform they already use (Slack, email, etc.). From there, it should take one click to open the interface, leave feedback or have a quick conversation with AI (bonus if you offer text or voice), and one click to close so the workflow moves forward. If it’s more complicated than that, your team will start to feel like they’re not saving any time. Ideally, the entire loop happens in one streamlined app.

It’s still far faster than writing from scratch, but it avoids the trap of pushing out hollow content just because the system can.

Content quality checklist for your team

• Does the system create space for strategy, persona knowledge, and real audience insights?

• Are you using AI as a conversation partner, not just a prompt generator?

• Do your workflows include prompts or questions that push depth—backing up claims, avoiding fluff, and speaking directly to audience challenges?

• Are you spending time with AI to tease out the nuances (what the audience complains about, how they talk, what they care most about)?

• Do you have editorial oversight to catch flat spots and sharpen the narrative?

• Are there built-in checkpoints (idea, outline, research, draft) where humans review and refine AI outputs?

• Are you scaling volume only once you’ve proven quality stays consistent?

• Do you track performance so you know that “more” actually translates into better results?

• Are you leaving room for authenticity—even imperfection—that makes the content feel human?

Content engineering isn’t about squeezing more out of your team or settling for fast but shallow output. It’s about building a system where volume and quality rise together. Done right, it multiplies your reach without thinning your credibility. You protect the human inputs—your strategy, your insights, your editorial eye—while letting AI handle the heavy lifting. That’s how you keep scaling without losing what makes your content worth reading in the first place.