Christopher S. Penn – Marketing AI Keynote Speaker

Almost Timely News: πŸ—žοΈ How to Create Unique, High-Quality Content with Generative AI (2024-05-05)

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Christopher S Penn
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Almost Timely News

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Content Authenticity Statement

100% of this week’s newsletter was generated by me, the human, though there are bountiful AI-generated examples in the walkthrough video. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

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Almost Timely News: πŸ—žοΈ How to Create Unique, High-Quality Content with Generative AI (2024-05-05)
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What’s On My Mind: How to Create Unique, High-Quality Content with Generative AI

Marcus Sheridan and Robert Rose both made the following comments:

Incorrect: “AI creates bad content.”
Correct: “Humans that don’t understand how to properly use AI create bad content.”

AI doesn’t create bad or good content – it only creates the most probable content. Whether or not it ends up “good” is entirely up to us.

These are both true statements, but the challenge with them is that there’s not a lot of, as Katie likes to say, “So What?” What do we do with these statements?

Well, first, we have to come up with what constitutes good or bad content. If you can’t define that, then you can’t judge whether AI is creating good or bad content. CMI defines content marketing (and by proxy, content) as follows:

Content marketing is a strategic marketing approach focused on creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience β€” and, ultimately, to drive profitable customer action.

So, great content should be valuable, relevant, and consistent, made for a clearly defined audience, with the intent of driving useful action. That’s a decent starting point.

Can AI do this? Certainly, consistent is no problem. Machines can scale the creation of content so that you have a never-ending amount of it. That leaves relevant and valuable, for a clearly defined audience, so that’s where we’ll start our exploration.

First, we should recap how generative AI – large language models in particular – generate anything. As Robert pointed out, AI models generate based on probabilities. Inside a model is a library of statistical data, huge piles of numbers that document the relationships among pieces of words, words, phrases, sentences, paragraphs, and documents.

In fact, in recent academic papers that study how large language models actually work, about 5% of the model is what’s called a retrieval head, a part of the statistical library that has pointers to the rest of the library. The best analogy for this mechanism is a real library. Suppose you walked into a library looking for a book, and you walked up to the librarian and said, “Hi, I need help finding a book.”

The librarian might naturally say, “Great, what kind of book?”

If you answered, “Oh, you know, a book,” what kind of result are you going to get? Yeah, you’re walking home with the nearest book to the librarian, which is probably not what you wanted.

If you answered, “Oh, I’m looking for some 18th century romance novels,” you’re going to get directed to a specific shelf within the library, and if the librarian is bored, they might go and get you a selection of books.

If you placed a book on the counter and said, “I need volume 2 in this series”, you’re going to get volume 2, assuming the library has it.

We know today’s biggest, best models like Claude 3 Opus, Google Gemini 1.5, and probably GPT-4-Turbo all likely use the latest model architectures, which means they have a small staff of librarians waiting to help you, with a head librarian who will direct you to the appropriate subordinate librarians based on your needs. If you go in asking for cookbooks, the head librarian will route you to the librarian who knows the cooking section well, and so on.

Great, so what does this have to do with building valuable, relevant content for a clearly defined audience? It’s exactly the same thing. We need to know what constitutes valuable, relevant content, and we need to know who the clearly defined audience is. If we don’t have either of those things defined, then when we approach a large language model to generate content, it’s going to generate content that’s not valuable or relevant.

Valuable content itself is too vague. What constitutes value? What makes content valuable to someone? Generally speaking, I’ve always gone by the 3Es of content – entertaining, emotionally engaging, or educational. If your content doesn’t hit at least one of these, it’s not going to resonate. People want to feel stuff when they consume content, which is why they watch Netflix more than C-SPAN. People want to be entertained and educated, learn how to do things, learn how to make their lives easier. So valuable content should hit at least one of the 3 Es, two out of three ain’t bad, and the trifecta is your goal as a content creator.

Relevant content is entirely based on the target audience. You can’t create relevant content if you don’t know who the audience is. This is where you create an ideal customer profile of some kind so that when you generate content with a large language model, it creates content that’s highly relevant to that person. We talked about how to create an ideal customer profile along with a step-by-step tutorial about a month ago in this issue.

One other aspect of content that is part of relevant and valuable is uniqueness. Very often, people value that which is scarce and unique, which means if you’re invoking very broad generalities with generative AI, you’re going to create fairly generic, not unique content.

Let’s look at a step by step process for generating unique, high quality content. We’ll use Robert and Marcus as our ideal customer profiles as our starting point, and we’ll tackle the topic of creating great content on LinkedIn as the kind of content we want to generate. How do we do this?

First, we prime the model by having it define an ICP, then we load their profiles and have the model build an ICP from that.

Once that’s done, we re-prime the model to come up with general LinkedIn content strategy guidelines.

Then we provide a LOT of knowledge from LinkedIn’s engineering blog about how LinkedIn actually works.

From that, we then employ contrastive prompting to generate a content outline, which may take a couple of iterations.

And once that’s done, we generate the actual content.

I recommend you watch the accompanying video to see each of these steps play out.

When we’re done, we have some really nice content that’s much more unique, highly relevant, probably valuable, and created for a specific target audience. Now, is this content that’s right for everyone? Nope. It’s made for Marcus and Robert, not for me, not for you, not for anyone except them. Are there parts of the content that are relevant to all of us? Sure. But the process of making unique, valuable content inherently makes content that’s most valuable to the target audience – which means it’s less valuable to everyone not in that audience.

That’s how to use generative AI to create great content.

And shameless plug, if you want help with building your generative AI systems and processes, this is literally what my company does, so if getting started with this use of generative AI is of interest, hit me up.

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ICYMI: In Case You Missed it

Besides the newly updated Generative AI for Marketers course I’m relentlessly flogging, Katie and I had a great discussion this week on AI ethics.

Skill Up With Classes

These are just a few of the classes I have available over at the Trust Insights website that you can take.

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Advertisement: Generative AI Workshops & Courses

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If you work at a company or organization that wants to do bulk licensing, let me know!

Get Back to Work

Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.

What I’m Reading: Your Stuff

Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.

Social Media Marketing

Media and Content

SEO, Google, and Paid Media

Advertisement: Free Generative AI Cheat Sheets

The RACE Prompt Framework: This is a great starting prompt framework, especially well-suited for folks just trying out language models. PDFs are available in US English, Latin American Spanish, and Brazilian Portuguese.

4 Generative AI Power Questions: Use these four questions (the PARE framework) with any large language model like ChatGPT/Gemini/Claude etc. to dramatically improve the results. PDFs are available in US English, Latin American Spanish, and Brazilian Portuguese.

The Beginner’s Generative AI Starter Kit: This one-page table shows common tasks and associated models for those tasks. PDF available in US English (mainly because it’s a pile of links)

Tools, Machine Learning, and AI

All Things IBM

Dealer’s Choice : Random Stuff

How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

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Events I’ll Be At

Here are the public events where I’m speaking and attending. Say hi if you’re at an event also:

  • Australian Food and Grocery Council, Melbourne, May 2024
  • Society for Marketing Professional Services, Los Angeles, May 2024
  • MAICON, Cleveland, September 2024
  • MarketingProfs B2B Forum, Boston, November 2024

There are also private events that aren’t open to the public.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn

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