Almost Timely News: ??? How to Create Unique, High-Quality Content with Generative AI (2024-05-05)
Almost Timely News: ??? How to Create Unique, High-Quality Content with Generative AI (2024-05-05) :: View in Browser
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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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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 — 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.
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What I'm Reading: Your Stuff
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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)
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See you next week,
Christopher S. Penn
GEN AI Evangelist | #TechSherpa | #LiftOthersUp
6 个月Fascinating stuff on leveraging AI's content prowess. Intriguing process for originality, eh? AI-human synergy shows tantalizing potential. Christopher Penn
Branding You as an Authority in Your Niche | Helping You Build a Lead Flow System with LinkedIn | Business Coaching for High-Ticket Coaches & Consultants | Creator of the Authority Brand Formula? | California Gal ??
6 个月That sounds intriguing. AI in content creation is the future. Looking forward to it
Head of Lifecycle Marketing, Portfolio at Atlassian
6 个月As always, you have the BEST analogies ?????? The library and librarian are helpful ways to think about prompts! I think the other thing that stands out in this post is that it takes quite a bit of up-front investment to make the output good, and most people are looking for quick or cheap wins. It's like walking in and asking for the cliff notes of the books vs. actually reading them, or going on GoodReads and sorting through all the ratings and reviews before requesting a book at the library ?? By the time you do all of that work, you probably could've just roamed the library until something caught your eye ??
CEO at Cognitive.Ai | Building Next-Generation AI Services | Available for Podcast Interviews | Partnering with Top-Tier Brands to Shape the Future
6 个月Insightful perspective on AI-assisted content creation. Intriguing to see real-world examples. What excites you most about this new frontier? Christopher Penn
Next Trend Realty LLC./wwwHar.com/Chester-Swanson/agent_cbswan
6 个月Good to know!.