This is the fourth post in a series of five on AI. In my last post, I proposed a framework for AI prompt writing. But before you can follow a prompt framework, you need to know what to ask and how to evaluate its response. This is where subject matter expertise and critical thinking skills come in. A reason we need to keep humans in the loop when working with large language models (LLM) like ChatGPT (Copilot), Gemini, Claude, and Llama.

Will we all be prompt engineers?

Prompt engineering is promoted as “the hot, new high-paying career.” Learning AI prompt techniques is important but doesn’t replace being a subject matter expert. The key to a good prompt is more than format. As I described in my post on AI prompts, you must know how to describe the situation, perspective, audience, and what data to use. The way a marketer or manager will use AI is different than an accountant or engineer.

You also must know enough to judge AI output whether it’s information, analysis, writing, or a visual. If a prompt engineer doesn’t have subject knowledge they won’t know what AI got right, got wrong, and what is too generic. AI is not good at every task producing general and wrong responses with the right ones. With hallucination rates of 15% to 20% for ChatGPT, former marketing manager Maryna Bilan says AI integration is a significant challenge for professionals that risks a company’s reputation.

AI expert Christopher S. Penn says, “Subject matter expertise and human review still matter a great deal. To the untrained eye, … responses might look fine, but for anyone in the field, they would recognize responses as deeply deficient.” Marc Watkins, of the AI Mississippi Institute says AI is best with “trained subject matter experts using a tool to augment their existing skills.” And Marketing AI Institute’s Paul Roetzer says, “AI can’t shortcut becoming an expert at something.”

Prompt engineering skills are not enough.

As a college professor, this means my students still need to do the hard work of learning the subject and discipline on their own. But their social feeds are full of AI influencers promising learning shortcuts and easy A’s without listening to a lecture or writing an essay. Yet skipping the reading, having GPT take lecture notes, answer quiz questions, and write your report is not the way to get knowledge into your memory.

Some argue that ChatGPT is like a calculator. Yes and no. This author explains, “Calculators automate a . . . mundane task for people who understand the principle of how that task works. With Generative AI I don’t need to understand how it works, or even the subject I’m pretending to have studied, to create an impression of knowledge.”

My major assignments are applied business strategies. I tell students if they enter my assignment prompt into ChatGPT and it writes the report for them then they’ve written themselves out of a job. Why would a company hire them when they could enter the prompt themselves? That doesn’t mean AI has no place. I’ve written about outsourcing specific tasks to AI in a professional field, but you can’t outsource the base discipline knowledge learning.

AI can assist learning or get in the way.

I know how to keep humans in the loop in my discipline, but I can’t teach students if they outsource all their learning to AI. Old-fashioned reading, annotating, summarizing, writing, in-person discussion, and testing remain important. Once students get the base knowledge then we can explore ways to utilize generative AI to supplement and shortcut tasks, not skip learning altogether. We learn through memory and scientists have studied how memory works. Used the wrong way AI can skip all stages of learning.

I remember what it was like being a student. It’s very tempting to take the second path in the graphic above – the easiest path to an A and a degree. But that can lead to an over-reliance on technology, no real discipline knowledge, and a lack of critical thinking skills. The tool becomes a crutch to something I never learned how to do on my own. My performance is dependent on AI performance and I lack the discernment to know how well it performed.

Research skills in searching databases, evaluating information, citing sources, and avoiding plagiarism are needed to discern AI output. The online LLM Perplexity promised reliable answers with complete sources and citations, but a recent article in WIRED finds the LLM search engine makes things up and Forbes accuses it of plagiarizing its content.

A pitch from OpenAI selling ChatGPT Edu, says, “Undergraduates and MBA students in Professor Ethan Mollick’s courses at Wharton completed their final reflection assignments through discussions with a GPT trained on course materials, reporting that ChatGPT got them to think more deeply about what they’ve learned.”  This only works if the students do the reading and reflection assignments themselves first.

Outsourcing an entire assignment to AI doesn’t work.

A skill I teach is situation analysis. It’s a foundation for any marketing strategy or marketing communications (traditional, digital, or social) plan. Effective marketing recommendations aren’t possible without understanding the business context and objective. The result of that situation analysis is writing a relevant marketing objective.

As a test, I asked ChatGPT (via Copilot) to write a marketing objective for Saucony that follows SMART (Specific, Measurable, Achievable, Relevant, Time-bound) guidelines. It recommended boosting online sales by targeting fitness enthusiasts with social media influencers. I asked again, and it suggested increasing online sales of trail running shoes among outdoor enthusiasts 18-35 using social media and email.

Then I asked it to write 20 more and it did. Options varied: focusing on eco-friendly running shoes for Millennials and Gen Z, increasing customer retention with a loyalty program, expanding into Europe, increasing retail locations, developing a new line of women’s running shoes, and increasing Saucony’s share of voice with a PR campaign highlighting the brand’s unique selling propositions (USP). It didn’t tell me what those USPs were.

I asked Copilot which is best. It said, “The best objectives would depend on Saucony’s specific business goals, resources, and market conditions. It’s always important to tailor the objectives to the specific context of the business. As an AI, I don’t have personal opinions. I recommend discussing these objectives with your team to determine which one is most suitable for your current needs.” If students outsource all learning to LLMs how could they have the conversation?

To get a more relevant objective I could upload proprietary data like market reports and client data and then have AI summarize. But uploading Mintel reports into LLMs is illegal and many companies restrict this as well. Even if I work for a company that has built an internal AI system on proprietary data its output can’t be trusted. Ethan Mollick has warned that many companies building talk-to-your document RAG systems with AI need to test the final LLM output as it can produce many errors.

I need to be an expert to test LLM output in open and closed systems. Even then I’m not confident I could come up with truly unique solutions based on human insight If I didn’t engage information on my own. Could I answer client questions in an in-person meeting with a brief review of AI-generated summaries and recommendations?

AI as an assistant to complete assignments can work.

For the situation analysis assignment, I want students to know the business context and form their own opinions. That’s the only way they’ll learn to become subject matter experts. Instead of outsourcing the entire assignment, AI can act as a tutor. Students often struggle with the concept of a SMART marketing objective. I get a lot of wrong formats no matter how I explain it.

I asked GPT if statements were a marketing objective that followed SMART guidelines. I fed it right and wrong statements. It got all correct. It also did an excellent job of explaining why the statement did or did not adhere to SMART guidelines. Penn suggests “explain it to me“ prompts to tell the LLM it is an expert in a specific topic you don’t understand and ask it to explain it to you in terms of something you do understand. This is using AI to help you become an expert versus outsourcing your expertise to AI.

ChatGPT can talk but can it network?

Last spring I attended a professional business event. We have a new American Marketing Association chapter in our area, and they had a mixer. It was a great networking opportunity. Several students from our marketing club were there mingling with the professionals. Afterward, a couple of the professionals told me how impressed they were with our students.

These were seniors and juniors. They had a lot of learning under their belts before ChatGPT came along. I worry about the younger students. If they see AI as a way to outsource the hard work of learning, how would they do? Could they talk extemporaneously at a networking event, interview, or meeting?

Will students learn with the new AI tools that summarize reading, transcribe lectures, answer quiz questions, and write assignments? Or will they learn to be subject matter experts who have discerned via AI Task Frameworks and AI Prompt Frameworks the beneficial uses of AI making them an asset to hire? In my next post, the final in this 5 part AI series, I share a story that inspired this AI research and explore how AI can distract from opportunities for learning and human connection.

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