AI fluency is a key content skill

Technology has laid the foundation for modern marketing, and generative AI is reinforcing the walls. Today’s marketing teams are revolutionizing their strategies using generative AI tools. Many are already seeing the results. According to a recent McKinsey survey, 34% of respondents report using the technology in marketing and sales. Content generation is the most popular use case.

That means we, the human marketers, must hone our ability to communicate well with our machine colleagues. As writers, we need to learn how to “speak” AI with better AI content prompts. Once you master the basics, your better prompts will more quickly and effectively produce assets that meet the needs of the business.

Let’s look at a few ways to improve your generative AI content prompts.

Best practices for writing generative AI content prompts

1. Get specific with your content prompts

Details matter when working with generative AI. The right request made in the right way improves your odds of getting text or images you can use in your blog post.

To illustrate, let’s see what happens with different kinds of AI content prompts. I am using image generation AI to demonstrate the different results from different prompts, but the same principles apply equally to AI text generation.

Check out the results I got on Image Creator from Microsoft Designer, chosen for this exercise because it is free and has a straightforward user interface. Image Creator uses Dall-E 3, the image generation platform from OpenAI, developer of the GPT-4 large language model (Microsoft is an OpenAI investor).

A content prompt is a set of instructions you give to the generative AI to shape its output. Prompts can be general, describing in simple terms what you want the image to show. For example, a prompt that says “father with his young son” gave me the following four options.

Because my request wasn’t super-specific, the AI made decisions about how to render these images based on what it knows from its training data. Those decisions were … odd. It’s a mystery why two of the four show the father and son flying, and all evoke Superman, either directly (as with the upper left) or more subtly (the upper right). Regardless, the mistake here is that I did not provide enough information, nor was I sufficiently specific.

As content marketers, we often know what we want or need, at least as it pertains to our brand’s editorial guidelines and visual style. We want to include those details in our prompts for better results.

So what happens when I iterate to include more specifics in my prompt? After multiple adjustments, I land on asking for a “photograph of a dad walking with his son on a rocky beach during the day.” Note I specify the type of image (photograph), the content (an adult male and a male child), the environment (rocky beach) and time of day (daytime). The resulting images are more down-to-earth—literally. I could also vary the results by adding specifics about the emotions I want to convey (e.g., a feeling of closeness between father and son) and different image styles (e.g., drawings vs. photographs).

It’s worth noting, however, that image generators can make mistakes as they interpret imagery in their training data and try to create new renderings. For example, the clasped hands in the upper left “Superman” image show blurring rather than precise distinctions between the fingers. In another example, one of my experimental images (not shown) had the father and child clearly walking together, but one facing forward and the other back.

Thankfully, there is a free “prompt book” that teaches creatives how to write better prompts for DALL-E. Originally developed for DALL-E 2, it includes some general prompt advice that is more broadly useful.

That brings me to my second best practice.

2. Use the right AI tools for the job.

Image Creator is just one of multiple generative AI tools for visual assets, just as ChatGPT is just one for text. At the risk of stating the obvious, different tools have different specialties.

For example, Midjourney is another generative AI for visual outputs. But unlike Image Creator, which is democratic about aesthetics, Midjourney specializes in “pretty” images. As a server-based tool, its user experience is also more challenging to navigate than others, and it requires a membership subscription to access.

Beyond aesthetics, there is the question of what kind of output you need. In the world of visuals, business content regularly incorporates visual representations of workflows, concepts, or frameworks. Check out the results from my prompt: “Create a stylish workflow graphic depicting the 6 steps of the content process represented horizontally: ideation, delegation, creation, production, promotion, and measurement. Use bold complementary colors and represent each step using a distinct image.”

This is a pretty typical result. I have tested writing more effective creative prompts in different generative AI tools, and they all seem to struggle with business images, especially when they contain text. (What is Deligmuation?)

Consider this alternative using the same prompt on Stable Diffusion, which usually creates elegant outputs.

I suspect the cause of these mixed results is a lack of training. Perhaps the AI developers have not yet seen the value of teaching them about business graphics. While most of these tools allow you to make edits to generated images, I think there is a better way to get top results using generative AI. Namely, you can prompt the AI tool to generate the components you need and then compile them in a design tool like Adobe Illustrator, which also lets you manually input text where and how you want it.

3. Fine-tune your AI content fact-checking.

Now for some bad news: AI content generators can produce images and text with mind-boggling speed. Yet, these AI models only mimic what they see in their training data. They’re unable to separate facts or reality from misinformation—for now. Consequently, AI-generated text and images can look and sound very authentic and authoritative yet contain nonsense. One example from my first experiments with ChatGPT included an AI-generated statement that the tool made up but attributed to Microsoft founder Bill Gates. Since then, OpenAI and others have trained their platforms so that users can no longer prompt the tools to quote a public figure or write something in that person’s “voice.” But the tools do still make mistakes.

So, be careful: Don’t publish AI-generated content or images without fact-checking it first.

4. Be wary of copyright.

Now for some bad news: the legalities around generative AI copyright are fuzzy. In the case of visual images, OpenAI, for one, specifies in its user disclosures that the images it creates are not necessarily unique. The same prompt from two different users may result in a similar or nearly identical image.

The platforms often stipulate that creators have the right to publish the text and images they generate, but buyer beware. The platforms argue that using copyrighted material to train large language models constitutes “fair use,” an acceptable though vague standard in copyright law. If judges disagree with that argument, however, complications could arise down the road about the legal status of images and text produced with the help of generative AI.

The rights issues are not trivial. But they also won’t be 100% clear for some time, and the commercial market is marching forward anyway. As for their quality, generative language and image AI tools will continue to improve as people use them. Experimenting with them can give you the knowledge and experience to perfect your AI content prompts. You’ll also develop an informed opinion about when to use them and when human creativity is the better option.

Ask the Content Strategist: FAQs about generative AI and content prompts

How can companies limit the risks of AI content creation?

Companies can limit risks by establishing clear guidelines on AI usage, including transparency about AI-generated content and adherence to copyright laws. They should also implement robust fact-checking processes to limit the risk of disseminating misinformation.

What rules should content teams establish to govern the use of generative AI?

Content teams should establish a clear set of ethical guidelines on how they will and will not use generative AI in their processes. The guidelines should reflect current thinking about copyright laws and promote transparency about AI-generated content. By establishing these rules, content teams can harness the power of generative AI while maintaining the integrity and credibility of their work.

How do different generative AI tools compare to each other?

Generative AI tools vary significantly in focus, usability, style, and quality. Some AI text generators, for example, focus on short-form content like email subject lines, social copy, web banner ads, and mobile texts. Others promise to draft long-form, SEO-optimized blog posts and pillar pages. Moreover, some are free to use, and some require a subscription. That variation — including cost — makes it essential that you know an AI tool’s pros and cons before you commit to a subscription.

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The post How To Level Up Your Content Prompts for Generative AI appeared first on Contently.

This has massive implications for our culture and society. It means we must prioritize AI ethics in a way we never have before. Which forces us to ask the question: What are some ethical considerations when using generative AI?

Consider some of the following questions while pondering this:

• Who owns the copyright to AI-generated content?
• How will we judge human creation vs. machine creation?
• Who monitors the output of generative AI?
• What ethical standards should be present to ensure responsible content creation?
• How will we keep machines accountable for what they produce?

Why does AI ethics matter?

AI is increasingly involved in making the decisions that affect our everyday lives, such as whether we’re approved for a loan or which ads we see online. These outputs significantly impact our well-being and how we perceive the world. That’s why robust ethical principles are necessary to guide AI development and implementation. When AI applications improve fairness, equity, and privacy in society, they can be an extremely useful and productive cornerstone. However, if it’s used maliciously, it could be detrimental. AI’s output also must be generated responsibly.

For example, using generative AI content to produce articles or videos could allow bad actors, authoritarians, and even governments to sow division. As we’ve seen with elections and social media, this division causes damage to society and social values.

Social algorithms creating polarization is just one ethical concern about the proliferation of AI. The stakes are high. Ethical standards for the use of AI will be crucial for monitoring output and evaluating if the content it generates is accurate and reliable.

Additionally, commercial interests often drive the creation of AI. There’s a real risk that AI will be developed primarily to maximize profits rather than benefit humanity. This, again, raises a red flag. We must consider the ethics of generative AI’s implementation as it advances.

How can we approach the ethics of generative AI?

The ethical implications of AI can be addressed in several ways. One approach is through policies and regulations that govern the use of AI and integrate them into the legal and regulatory system.

Another method encourages companies and individuals to adopt ethical principles voluntarily for generative AI content creation and use.

So how is AI being governed?

• The US government released an AI Bill of Rights that focuses on its development and implementation principles. These principles guide agencies in using AI and ensure that AI is developed and used responsibly.
• The United Nations has also issued a set of principles for the development and use of AI, which is intended to promote the responsible development and use of AI and focus on human rights.
• The European Union has issued several documents on AI, including ethical guidelines for trustworthy AI. These guidelines are intended to ensure that AI is developed and used in a way that is ethically sound and respects fundamental rights.

In addition to world governments, several non-profit organizations are working to promote the responsible development and use of AI to mitigate its risks:

• The Partnership on Artificial Intelligence is a non-profit organization bringing together companies, academics, and other experts to discuss the ethical implications of AI and to create best practices for its development and use.
• The Center for Democracy & Technology is a non-profit organization promoting the responsible use of technology, including generative AI.
• The Electronic Frontier Foundation is a non-profit organization that works to protect civil liberties in the digital world, including the rights of people affected by AI.

What guidelines signify responsible generative AI content creation?

When using generative AI content, it’s essential to know your actions’ potential implications and consequences. You need a code of ethics to guide your behavior as an individual and an organization. Consider the following:

1. What are the possible risks and implications of creating content with generative AI?
2. How might your content be misused or misinterpreted?
3. What could the potential negative impacts be on individuals or groups of people?
4. Are there any risks to public safety that need to be considered?

These questions are paramount to the use of generative AI content. Anything you create should be ethically sound and responsible for you, your company, and those who consume it. When organizations abide by ethical guidelines, they minimize negative impacts derived from generative AI.

How can content marketers uphold AI ethics and use generative AI responsibly?

Generative AI has the potential to revolutionize content marketing, but several ethical considerations must be addressed. In addition to being aware of the potential implications of the content, it’s also essential to follow a set of best practices when using generative AI in content marketing.

1. Define goals and objectives.

Clearly define the goals and objectives of your content marketing campaign before using generative AI. Create a brief for your content campaign that includes your goals, key pillars, topics to be covered, personas, keywords, and tone of voice. This will help you get the most out of your generative AI content production. The better your brief, the better the output.

2. Establish guidelines.

Create rules or guidelines that will be used in generative AI production. Add a section on generative AI to your content style guide. Make it ethical. Provide do’s and don’ts. Add advice on how to make the output more successful, what differences might occur between briefs based on content type, and what to evaluate once the product has been delivered. Make sure to include an editorial step that requires your team to search for data and respected publications that validate your key points.

3. Establish a robust fact-checking process.

“Hallucinations” represent one of the emerging risks of using generative AI to draft text-based content such as emails, blog posts, and social messages. These factual-sounding statements are a common problem with generative AI. When the inaccuracy is obvious, content creators can quickly identify and edit it out. But it is not always that easy.

Generative AI is already producing text with falsely attributed quotes, invented data, and supposed “findings” that sound plausible but aren’t connected to real research. Organizations need fact-checkers with a keen eye and robust checking processes to suss out misinformation and remove it from AI-generated content before it damages your brand.

4. Monitor regularly.

Regularly monitor the generative AI content created to ensure that it meets your standards and objectives. Sometimes an editorial process won’t be enough. Maybe one piece fits your goals after editing, but it’s not creating a holistic narrative with the rest of your content. Make sure all parts fit the ethical standards you’ve set individually and collectively in your marketing campaigns.

By following these best practices, you can help ensure that using generative AI in content marketing is safe, responsible, and effective. Generative AI has the potential to revolutionize content marketing, but several ethical considerations need to be taken into account before we outpace ourselves in innovation.

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The post The Ethics of Generative AI and Responsible Content Creation appeared first on Contently.

That makes sense, given that content creation is one of the most straightforward, free, and widely available applications of the technology. But that’s not the only way to use AI.

Look at generative AI adoption across industries and functions, and it becomes clear that people are getting quite creative with it. Use cases span well beyond text and image generation. So marketers who better understand how the technology may touch their lives stand to benefit, finding new ways to use AI to save time and boost efficiency.

Let’s look at a few examples by industry. But first, it helps to understand where generative AI falls in the taxonomy of AI technology.

Analytical vs. Generative: AI Takes on Different Functions

Even data scientists disagree on an exact taxonomy for all the types of AI available right now. For our purposes, we’ll keep it simple and focus on two distinct categories.

• Analytical AI leverages AI techniques such as machine learning with the end goal of identifying and then flagging patterns in data sets. Depending on the purpose of the AI, pattern recognition may call attention to dynamics that humans would not be able to see, given all the noise. It also may be able to predict future events based on historical precedent seen in data. The original version of IBM Watson is an analytical AI question-answering machine.
• Generative AI also leverages AI techniques and identifies patterns—but with the goal of using what it knows about those patterns to create new, original outputs. The large language model GPT-4 is a generative AI that has spawned scores of apps trained to produce outputs in the form of text, images, video, and others.

Businesses are applying both forms. They can be used in combination to automate parts of a workflow or to identify solutions to a problem. The following examples highlight creative uses of generative AI—but I highlight their analytical counterparts as well because the lines may likely blur over time.

Financial Services and GenAI Adoption

Generative AI and financial institutions have made news in recent months. In the context of ChatGPT, many U.S. banks have banned it due to security fears or concerns over leaks of proprietary data. Organizations like Bank of America, JP Morgan Chase, and Wells Fargo have been particularly vocal. That does not mean they are eschewing the technology entirely.

On the contrary, traditional financial institutions, as well as service providers and consumer-facing financial software companies, are finding interesting ways to deliver value using generative AI.

How Generative AI Helps Traditional Banks

Long before ChatGPT entered the market, banks used natural language processing (NLP) and chatbots to supplement service delivery. That has since accelerated with the emergence and enhancement of large language models.

Since 2018, for example, Bank of America has delivered customer service through a language generation-powered virtual assistant called Erica, which provides a range of services like tracking payments, executing transactions, or flagging duplicate charges or increases in monthly bills.

While Erica appears to be a home-grown solution, other banks have partnered with NLP providers to deliver similar services. Wells Fargo, for example, has worked with AI firm Kasisto, which has a proprietary large language model trained on the narrower parameters of financial content, to ensure it produces relevant communication for retail banking customers.

Financial Technology and Generative AI

The bank-to-consumer relationship is only one area of innovation for using generative AI. Technology providers in the financial sector are also seeking ways to leverage the technology. For example, AI has enabled Intuit—the maker of the QuickBooks accounting application and the TurboTax tax preparation software—to power its help and FAQ functionality for several years.

As of June 2023, a finance-specific large language model will supplement those functions. According to Intuit, its LLM will soon help solve tax, accounting, cash flow, and personal finance problems.

Generative AI Industry Use Cases in Healthcare

The healthcare sector generally consists of three primary arms: the providers (e.g., doctors, therapists, and health facilities); the financial (payer organizations, including insurance companies and the Centers for Medicare & Medicaid); and discovery (e.g., pharmaceutical and research organizations). Creative applications of generative AI are surfacing across all three. Here, we’ll highlight a few in the provider and discovery spaces:

How Providers Can Leverage GenAI for Records and Notes

Providers are adopting generative AI not just to automate parts of marketing, for example, but also to streamline patient communication. Microsoft recently announced a deal with the medical records software giant Epic to integrate OpenAI’s GPT-4 (Microsoft is a major owner/investor) into Epic’s platform with the goal of automatically generating email responses to patient messages.

Patient communications represent a logical, industry-specific extension of generative AI’s language-generating capabilities. On the other hand, diagnostics may seem like more of a reach—yet some interesting generative AI use cases are emerging here as well.

For example, a medical device company called Butterfly Network has developed a handheld ultrasound device that allows medical professionals to capture ultrasound images at the point of care and upload them to the company’s cloud platform. A radiologist still needs to read them, however—which can be a major bottleneck in care settings. Butterfly has begun using generative AI to train more clinicians to read basic ultrasound images so that specialists can focus on more challenging cases and case reviews.

Generative AI Helps Speed Up Pharmaceutical Development

The pharmaceutical industry is always looking for new molecules to explore as potential treatments for disease. I’ve already written about the efforts of a group of researchers at the University of Florida partnering with Nvidia, an AI technology provider, to develop a health-focused large language model that can analyze language data in electronic health records. One potential use of GatorTron, as the model is known, is to look for previously unseen patterns in people with the same condition. The hope is that this data can help reveal new treatment options.

Generative AI Industry Use Cases in Retail

Retail marketing and advertising teams have been some of the earliest adopters of generative AI. Look beyond customer-facing departments, and the tools offer compelling business opportunities around product design and conceptualization.

Generative AI in Retail Product Design

Generative AI used in product design enables manufacturers to optimize materials and component parts to reduce product weight or manufacturing costs, according to McKinsey. While computer-aided design software has been part of the product design process for years, generative AI brings extra automation and machine learning features to the table.

In retail fashion, for example, a collaboration platform called CALA has integrated DALL-E 2 into its functionality. Now fashion designers can share mood boards and pictures of retro designs from the past and add palettes to develop multiple versions of a clothing item.

The technology can also take prompts or hand over to a designer to modify and adapt one or more design options before moving into prototype development or even production.

Customer-led customization is also becoming more widely available. A company called ThreeKit has been helping retailers show their products in 3D on their websites. A recent integration with ChatGPT adds new capabilities around product customization using different components within a retailer’s product catalog.

Back to Content Marketing

By highlighting all the non-content ways that generative AI applies across industries, we hope to make clear that the technology is not just changing some areas of business—it’s changing all of them. And that can be positive.

Creation is not just about surfacing patterns or combining parts that already exist into a new whole. Yes, those strategies can reveal a new innovation. But fine-tuning it, testing it, evolving it to solve a concrete problem in a way that engages the customer’s imagination—all of these activities require human mediation.

That’s true in marketing, too. Your ability to differentiate yourself and your brand has always depended on coming up with fresh ideas and executing on them. Now, you’ll do it with generative AI on your side.

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The post Beyond Words: The Expansive Impact of Generative AI in Various Industries appeared first on Contently.