Eight in 10 marketers say they plan to create more content next year than they did this year, according to Parse.ly’s 2022 Content Matters report.
This is both a staggering figure and one that’s not surprising for content creators — it’s what they’ve grown accustomed to.
Every year, content demands rise and diversify, but the strategies behind how new content is built don’t change much. When businesses needed to invest in more videos and podcasts, content teams adapted.
When longer-form LinkedIn thought leadership grew in popularity, creatives stepped up to the challenge. Content producers can build great content quickly, but they also feel the strain of creating assets without much time to take a breath.
Advancements in content forms can be great for marketing teams, but rarely are there advancements that give creators more time to avoid burnout or ideate better.
Now, generative AI is here, and it’s positioned to be one of these time-saving tools.
The immense strain that content teams are under to keep up has been a driving force behind the“adoption of AI by the enterprise.
And this implementation aims to give enterprise content teams and professionals more time to generate novel ideas by reducing the time it takes to execute them.
Let’s take a look at how AI is improving content operations and how creative teams can use it to streamline their workflows.
Generative AI is a type of artificial intelligence that can take human inputs and create something entirely new, like art, writing, video, or audio. Given a prompt, it generates a new creation. Generative AI learns by leveraging natural language learning models and consuming content. It picks up patterns in those language models and leverages them to complete thoughts or repackage ideas provided by a human. Because it makes conveying ideas easier and faster, generative AI has the potential to be a transformative technology for productivity and creativity.
Here’s how the process works: Large language models are given a string of text and predict the most likely next word. These models consume a huge portion of the information on the internet — about 10-20% — giving them a baseline of context on various topics in various languages.
Through exposure to this large amount of content, the models learn how humans naturally speak, write, and create things like art. They then complement that baseline with whatever prompt or context you give it. As you give the AI models more context, their outputs become better.
The two main models used in generative AI are natural language processing (NLP) and artificial neural networks (ANN). NLP models use rules to learn from existing text, while ANNs use data to create new relationships between elements.
Accessing Generative AI
Generative AI can be accessed directly through a large language model or an AI application like Jasper. Applications like these pull from multiple AI models with a specific use case in mind. They select the right models for the right circumstances and combine them to serve the user’s needs more effectively.
Over the next few years, there will likely be an emergence of a wide variety of AI models with varying strengths and specializations.
Application layers will also evolve and deepen in functionality, in addition to using data to fine tune their models and create increasingly efficient outputs. That’s how the technology behind generative AI works, but it’s much more intuitive in practice.
As a creator, you can use AI as an assistant to help you convey your ideas simply by giving it prompts.
All About Prompts
Prompts are the input that people give the AI to set the direction for what it generates. Prompts themselves are just natural language, i.e., the normal speech patterns that humans use in everyday communications, so the barrier to entry is low. But there is skill in developing great prompts that determine the quality of an output that AI can generate.
Below are a set of prompts put into Jasper, a generative AI tool, and the results they create. In the examples below, the human-created prompts are in bold, and the AI output is in italics.
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Write a sentence on Audre Lorde.
Audre Lorde was an influential American writer, poet, and civil rights activist who spokepowerfully about race, gender, and sexuality -
Explain the difference between a data breach and a hack.
A data breach is an incident where sensitive, confidential, or otherwise protected data has been accessed by an unauthorized user. A hack is an attempt to exploit a system’s vulnerabilities in order to gain access to information or disrupt its operations. -
Do you gear your content teams toward written how-to content optimized for search or thought-leadership and entertainment geared to spread via social media and word of mouth? Rephrase my last sentence
Are you optimizing your content teams for search engine visibility through how-to content or aiming to spread thought-leadership and entertainment via social media and word of mouth?
Prompts can be simple, but they are best when they are intentional.
As you can see, simply changing the keywords from “peaceful, calm” to “bright, rich, spirited” creates an entirely different feel to the outcome.
Alterations in prompts can be made to the core directive:
- Write the next line.
- Rephrase this line.
- Extend this paragraph.
Then, add descriptors to the directive (“peaceful”, “colorful”), tone of voice (“witty”, “educational”, “dry”) inspiration (“in the style of Salvador Dali”, “in the voice of Oprah”) and more.
Now that you’ve got the basics of how AI works and some terminology, let’s explore how you can use generative AI to scale your marketing and content strategy.
