In today’s episode, I explain why generative AI outputs can seem generic and watered down. The key is using detailed, specific prompts to provide enough context for the AI to generate high-quality, tailored content. With the right prompts, these tools can replicate writing styles, tones of voice, and target audiences. Tune in to learn how to create prompts that steer AIs away from bland, generic text.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In this episode, let’s talk about generative AI prompts and generic outputs. One of the things that people often say about generative AI, particularly large language models is how generic the text sounds. They say, Oh, it sounds so generic. Oh, it’s bland, it’s watered down. And that’s true from a certain point of view, from a certain type of usage of these models.
The way these models work, they are nothing more than prediction engines, right? They are nothing more than predicting the next token in a sequence of tokens. And if you recall, a token is basically a three or four letter fragment of a word. So the word of would be a complete word in the token. The word answer would be two tokens a and SW is the first token and ER as the second token.
When these models do their work, they’re predicting what’s going to be the next token. The way they do this is by looking at a token or in our case, you know, conceptually a word and saying what are the statistical probabilities of the next word in the sequence if I say, I pledge allegiance to the if you’re an American, the answer should be flag, right? If I say God save the depending on the frame of reference, it’ll either be the God save the king or God save the queen, right? If you’re in the UK.
And so that’s what these engines do. They just predict the next word. The reason they work so well is because they’ve been fed a lot of words and understand the statistical relationship of all the words around a word. So it’s not just the likelihood of the next word being what it is independently, it’s within a context.
If I say I’m brewing the it could be tea, it could be coffee could be kombucha, right? It could be the fall of capitalism. Whatever the case is, is going to be dependent on all the words around it. So if in the previous paragraph, I’m mentioning things like oolong, or Jasmine or Earl Gray, the presence of those words creates a statistical association for these tools to say, okay, you’re probably talking about tea. So the next word in the sentence, I’m brewing the statistically probably is going to be tea.
If I say, talking about Starbucks or Dunkin Donuts or Arabica versus robusta beans, there’s a good chance I’m talking about coffee, right. So the next word predicted would be coffee. I’m talking about hops and grain and mash, I’m probably talking about beer. That’s how these tools work.
So if you are getting generic outputs from your prompts, the problem is your prompt, the problem is that you are not being detailed enough in your prompt to be able to have the tool generate the outcome you want. These tools can generate very, very specific writing styles, tones of voice, specific content, but you’ve got to give them enough data to work with.
If you’re trying to have it replicate, say your writing style, you need to provide a lot of your writing style for it to understand – one blog post won’t cut it. You need to be thinking about 10 2030 1000 words of your writing style from in different formats, email, blog content, articles, interviews, so that the tool can can say, Okay, well, what are all the statistical associations in this large body of text, and that will capture what your probable writing style is.
The other thing that these tools are really good at is if you give them a target audience, maybe some words or phrases or paragraphs or documents, but here’s who our audience is. It then has additional data, additional associations that can make to be able to generate text that meets that need.
This is one of the secrets I talked about this recently on the Trust Insights live stream. This is one of the secrets to making tools like Claude to or chat GPT or whatever, deliver really high quality content, particularly sales content. If you feed these tools a lot of data, and they understand the outcome of what you’re trying to achieve, they will process that data really well – a two sentence prompt doesn’t cut it a two page prompt. Now you’re starting to get somewhere.
I did something recently where I took the LinkedIn profiles of 10 of our best customers. And I said, Okay, well help me construct a buyer persona. I’m providing all the data I’m providing a very specific focus. And I’m asking the tool to find associations and summarizations to distill out what a buyer persona is. It’s very straightforward to do that you can do that today with the tools that can handle more text GPT for and Claude to can handle a lot of text at a time.
So if you’re getting generic outputs, it’s because you’re putting in generic inputs. I there’s there’s no polite way of saying that it’s interesting. It’s, it’s almost the opposite of SEO. In SEO, we create text with keywords and phrases and things where we are trying to capture the most commonly used terms, right? best coffee shop in Boston, and so on and so forth.
With these large language models, using generic terms, highly searched terms is going to lead to highly generic outcomes, right? Because best coffee shop in Boston is not particularly specific. See, if you said write a blog post about the best coffee shop in Boston, you’re going to get some very generic stuff because mathematically, it’s pulling from all of the probabilities around each of those words.
If you said, write a blog post about the best coffee shop in Boston that serves a single shot espresso that is made with only beans from Kenya. Now you’re getting a lot more specific and what’s happening is instead of that big pool of probabilities, every relevant word you add to prompt narrows the pool down, right? It shrinks the pool of eligible words eligible predictions it can make. When you do that, you end up with better text, you end up with better outputs.
So if you like, there’s too many boring words, add more words to your prompt that are very specific. Look at things like jargon, what are phrases that only people in your audience will say, I used to work in the financial aid world. There’s a piece of paper was used to be paper now it’s online, a document that the US federal government publishes called the FAFSA, the free application for federal student aid. Nobody talks about the FAFSA in any context, other than financial aid for college, right is not something you discuss on a Friday night for fun is not something that you that comes up in casual conversation, it is always about that topic.
And so that’s an example where you have a term that is so context specific. So it’s a piece of jargon that any language model will see that go, okay, this is a piece about financial aid, if you’re saying the word FAFSA, this is about financial aid. That’s what you got to do. That’s how you use these tools is you take these tools, and you give them very specific wording, the more relevant and specific words in your prompts, the better they will perform, the less generic they will be.
And if you give them things to copy, like your writing style, you will get better results. That’s the way it is with these tools. So give that a try. If you’re not happy with the outputs you’re getting from these these large language models and recognize that your prompts might need to be depending on the task you’re asking them, they might need to be pages long.
Now you might say, Well, then what’s the point? I could write that myself. Yes, you could write one post by yourself. The point is, if you want to be able to scale your content creation, then you invest the time upfront to build long, detailed, highly specific prompts so that then the tool can replicate and scale and do more without you once it understands who you are.
Anyway, that’s the topic for today. Thanks for tuning in. We’ll talk to you next time. If you like this video, go ahead and hit that subscribe button.
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