If ChatGPT can produce content on any topic, do we still need experts?

Some AI evangelists would have you believe that we don’t. They think generative AI can make anyone an expert in any field—eliminating the need for skill and experience.

But that’s not quite true. Generative AI can help anyone get a quick understanding of a topic at a surface level, and that’s a powerful equalizer. But AI content creation that produces real insight, the kind that’s valuable to other experts, requires deep mastery of a subject area. In short, as a B2B marketer you still need to collaborate closely with the subject matter experts inside (or outside) your organization, weaving their input into your AI-assisted content creation process.

With that in mind, let’s look at how domain expertise helps you get the most from generative AI.

Input: Know What to Ask for in AI Content Creation

Keep in mind one of the most basic principles of generative AI: What you get out of it depends on what you put in. Large language models (LLMs) recognize patterns in their training data and use that recognition to generate statistically probable outputs based on your prompt. Without specific direction, the text an LLM generates will essentially be an average of all the data it’s trained on.

Knowledge of basic prompt patterns is useful here. But you could be an expert-level user of generative AI for marketing and it would still be hard to match domain experts in the following areas.

Language

When you use the specialized language of a field in your prompt, the output tends to improve for two reasons—one obvious and one less so.

The obvious reason: Using specific language makes your prompt more precise. The more you speak the language of a field, the more successful and meaningful your prompts will be.

The less obvious reason: People who are highly knowledgeable in a field use language specific to that field—so when you use that same language in your prompt, AI taps into patterns found in their work, helping to elevate its own output to the same level.

A simple example: If you were to ask AI to explain how the stock market works, it would likely draw upon patterns learned from financial publications and Reddit posts alike in its response. (r/WallStreetBets, anyone?) But mention P/E ratio, and suddenly the patterns you’ve tapped into are coming from a much smaller set of sources, most of which were written by people who know what they’re talking about.

Takeaway: Talk to your SMEs to make sure you’re using the right language to prompt AI.

Specificity

Beyond the language they use, subject matter expertise lets users achieve a level of specificity in what they ask of generative AI. Ask for the top three trends in a field, for example, and an LLM will regurgitate content that’s already all over the internet. Ask it to elaborate on three trends that you identify, and you have a much better chance at creating uniquely insightful content.

There’s a useful prompting tip in here: Generative AI often produces its most original and creative content when you force it to go against the grain because it won’t fall back on thousands of mediocre posts about a topic in its training data. And who is most likely to identify a relevant trend or topic that flies under the radar? Probably not someone who learned about the field by reading a Wikipedia article that morning.

Takeaway: Get expert input before you use AI to create content when you’re still at the ideation stage.

Synthesis

Because of its pattern recognition abilities, generative AI shines at drawing connections between seemingly unrelated concepts and ideas. You can see this in action any time an LLM uses an analogy to explain a concept—although sometimes the AI’s own analogies are a little goofy. But an expert can suggest informed, natural connections others may not see, which is often enough to get an LLM to produce thoughtful and interesting content.

Takeaway: Conducting in-depth SME interviews to learn how the experts think and what connections they see is more crucial than ever.

Output: Know How to Evaluate and Improve on AI Content Creation

So, you’ve used your knowledge to coax generative AI into writing something interesting. But as all content marketers know, AI content creation still requires human oversight. Here are three areas where domain expertise (again, not AI or content expertise) is required to take an AI-generated draft across the finish line and sculpt it into content that’s worthy of your brand.

Fact-checking

AI still makes things up. And as its capabilities improve, its fabrications get more subtle. In the early days of GPT-3, most so-called AI “hallucinations” were obvious to a casual reader, or at least easy enough to disprove using Google. Nowadays, generative AI is prone to half-truths and statements that are just slightly off, making it more difficult to spot fabrications without domain knowledge.

Takeaway: Run AI-generated output by an SME for fact-checking—always.

Evaluation

Just as it often takes an expert to provoke AI into creating content that goes beyond bland neutrality, it can also take an expert to recognize when a piece of AI-generated content—especially one that’s well-organized and convincingly written—is not saying anything new or interesting. B2B marketers who use AI for marketing want to create thought-provoking, original content that grabs the attention of the professional audience that they’re targeting. It takes someone who actively engages with the ideas in that professional community to know if a piece of content really hits the mark.

Takeaway: Don’t stop at a quick sign-off for factual accuracy—ask your experts if AI-generated content is insightful and useful, and if not, what would improve it.

Refinement

One of generative AI’s strengths is its ability to apply feedback to improve upon and make changes to its outputs. Savvy AI users take advantage of this capability to engage in a back-and-forth dialogue: They provide feedback, request rewrites, ask questions, and even use techniques like asking the AI to interview them until it thinks it has enough information to create a draft.

Ethan Mollick, who writes about the effects of AI on work and education, uses the term “Cyborg” to describe those who take this collaborative approach to using generative AI. In his post “Centaurs and Cyborgs on the Jagged Frontier,” Mollick writes, “Cyborgs blend machine and person, integrating the two deeply … Bits of tasks get handed to the AI, such as initiating a sentence for the AI to complete, so that Cyborgs find themselves working in tandem with the AI.”

This Cyborg approach may be a hallmark of someone with expertise in generative AI, but its usefulness is limited unless you also have domain expertise—for all the same reasons that domain expertise helps you know what to ask for in your inputs. (And if you want AI to interview you, obviously it’s best if you know what you’re talking about.)

Takeaway: If you can, bring your SMEs directly into the content creation process. Letting them work directly and collaboratively with AI is one of the most powerful ways to use AI to improve your content.

For Great AI Content Creation, You Need Domain Experts

AI is like a mirror: It simply reflects what you ask for. That’s why it’s so important to bring SMEs into your AI content creation processes. Without them, AI will only continue to help you generate mediocre content that doesn’t elevate your brand.

Tendo can help bring content teams together and optimize your AI content creation processes. Talk to us about how to get more from your AI content.