It’s unsurprising that AI leans heavily on cliches. Having digested most of the internet as training data, it offers responses that are typically amalgamations of previously published material. And contrary to some of the more dystopian headlines out there, AI does not (yet) have original thoughts.

But the very skills that make generative AI a mediocre content creator make it an excellent editorial assistant. AI’s penchant for pattern recognition makes it highly skilled at things like calendar management, and its impressive synthesis skills mean you can whip up a dozen social media post variations from one original in seconds.

Here are a few ways generative AI can make your job a little easier, as well as some best practices for introducing the technology into your work routine.

How to use ChatGPT for content optimization and more

Below are four helpful use cases that generative AI may be able to take off your plate.

Use case 1: Setting up your content calendar

We’ve written previously about the power of a dialed-in content calendar—especially if you’ve got one of those clients who seems to push back their target publish date week after agonizing week. Whether you’re publishing sporadic social posts or managing a full-fledged content marketing machine complete with white papers, video projects, and infographics galore, an editorial calendar is critical for staying on track (and sane).

Content marketers can use ChatGPT (or your generative AI engine of choice; I’m currently partial to Claude) to input key parameters like target audience, desired content mix, and publishing frequency. You can then prompt the AI to come up with a comprehensive content calendar formatted with as many columns as you like.

Further, AI can help with workflow management by suggesting optimal project timelines across multiple stakeholders—saving you hours of working backward from moving-target deadlines in the aftermath of a client’s latest fire drill.

Use case 2: Nailing your content briefs

There’s a big difference between editing and proofreading, and AI can be an excellent second set of eyes for the latter—whether you need to ensure client communications are typo-free or ensure your content briefs contain enough information for creatives to work their magic.

Here’s an example prompt you can try with your next brief:

Use case 3: Suggesting optimizations and SEO tweaks

Content marketing is extremely fluid, especially in today’s day and age (nobody knows, for instance, what’s going to happen to SEO in the coming weeks and months). Today’s golden-ticket keywords may land you on page ten of SERP purgatory tomorrow.

AI can prove an ally here, too, helping you pivot and optimize as the SEO winds shift. For example, AI can audit existing content and provide data-driven suggestions to improve elements like titles, subheadings, readability, keyword usage and density, accessibility, etc. It can also help out with tasks like identifying internal linking opportunities.

Use case 4: Adapting content for omnichannel distribution

AI can help repurpose a long-form piece like a white paper into derivative assets like social media posts, email newsletter content, and ad copy while maintaining message consistency. It can also be a great way to generate multiple headlines for A/B testing in campaigns or craft more functional copy like meta descriptions, image alt text, or video transcripts.

How to improve content strategy with AI: 4 best practices

Of course, there are some important do’s and don’ts when mastering how to use ChatGPT for content marketing—and specifically for client-facing work. Below are four to keep in mind.

1. Provide clear and specific prompts.

AI works best when you give it detailed instructions and context. Be as specific as possible about what you want to generate, including any key talking points, desired tone, or formatting requirements. The more specific your prompts, the more likely the AI will deliver quality outputs.

2. Review all outputs with a human eye for detail—and common sense.

Think of AI as a starting point, not a final destination. Always carefully review any AI outputs before presenting them in client-facing materials or plugging them into your content strategy. Be sure to edit all AI-crafted social posts to ensure logic, flow, and an appropriate brand voice.

3. Don’t rely solely on ChatGPT content analysis.

These days, new AI tools are cropping up for content optimization, generation, and analysis on an almost daily basis. While ChatGPT is the most well-known, it’s worth exploring other options to find the ones that best fit your needs. Many AI platforms offer free trials, so you can test drive before committing.

4. Use AI responsibly.

There are certain things you should never share with AI—including sensitive or embargoed client data, proprietary information, personal details, and anything covered by an NDA. Err on the side of caution when it comes to data privacy and security.

If you’re a freelancer, you’ll also want to check in with each of your clients to see if they have a responsible AI policy. If you use vendors yourself, it’s a good idea to draft your own guidelines around issues like disclosure, copyright, and data handling. Also, be sure to review any new client contracts for clauses that dictate if/how you can use AI.

Finally, don’t forget to fact-check AI outputs. In a world in which Google is suggesting people “eat rocks” and lawyers are citing fake precedents in court, you don’t want to risk damaging your reputation—or your client relationships—by being sloppy.

The key is to view AI as a partner, not a replacement. Sidestep the cliches and use AI for its true strengths: speed, scale, and data synthesis. By freeing yourself from tedious tasks, you can focus on higher-level strategy and creative ideation—you know, the actually stimulating aspects of the “intricate mosaic” that is content marketing.

Ask the Content Strategist: FAQs about how to use ChatGPT for content calendars

What level of technical expertise is required to effectively use AI tools for content marketing?

You don’t need a computer science degree to learn how to use ChatGPT for content marketing. As long as you’re comfortable navigating basic software and have a general understanding of what AI can (and can’t) do, you should be able to get up and running pretty quickly.

What are some best practices for evaluating and selecting an AI tool or platform for content marketing purposes?

When choosing an AI tool, consider factors like pricing (including any usage limits or extra fees), available features and integrations, ease of use, and customer support. Look for tools that align with your specific needs and goals—whether that’s content calendar management, SEO optimization, or something else entirely. Take advantage of free trials to test out different options before committing.

Can AI tools be customized to fit specific industry needs or niches?

Absolutely! Many AI tools allow you to input your own data and parameters to tailor the outputs to your specific industry or niche. For example, you might provide the AI with examples of high-performing content in your field or a list of industry-specific keywords and phrases to incorporate. You can even play around with creating your own custom GPTs.

Keep up with the evolving world of generative AI and how it can help your marketing efforts by subscribing to The Content Strategist.

The post How To Use ChatGPT for Content Analysis and Optimization appeared first on Contently.

The end result was… mediocre. The AI could surface articles that were similar to ones the audience had engaged with in the past, but we couldn’t make it smart — which is another way of saying we couldn’t teach it to recognize the ineffable nature of a fresh idea or a dynamic way of talking about it.

Ultimately, my client pulled the plug on the AI project and eventually on the newsletter itself. But I’ve been thinking about that experience as large language models (LLMs) like GPT-4o by OpenAI continue to gain broader mainstream attention.

I wonder if we would have been more successful today using an API into GPT-4o to identify “good” articles.

GPT-4 underpins content AI solutions like ChatGPT and Jasper.ai, which have an impressive ability to understand language prompts and craft cogent text at lightning speed on almost any topic. But there is a negative side to content AI: the clever content they produce can feel generic and they often make stuff up. Impressive as they are in terms of speed and fluency, the large language models today don’t think or understand the way humans do.

But what if they did? What if AI developers solved the current limitations of content AI? Or, put another way, what if content AI was actually smart? Let’s walk through a few ways in which they are already getting smarter and how content professionals can use these content AI advances to their advantage.

5 ways content AI is getting smarter

To understand why content AI isn’t truly smart and how it’s getting smarter, it helps to recap how large language models work. GPT-4 and “transformer models” like Gemini by Google, Claude by Anthropic, or Llama by Meta are deep learning neural networks that simultaneously evaluate all of the data (i.e., words) in a sequence (i.e., sentence) and the relationships between them.

To train them, the AI developers used web content, which provided far more training data with more parameters than before, enabling more fluent outputs for a broader set of applications. Transformers don’t understand those words, however, or what they refer to in the world. The models can simply see how they are often ordered in sentences and the syntactic relationship between them.

As a consequence, generative AI works today by predicting the next words in a sequence based on millions of similar sentences it has seen before. This is one reason why “hallucinations” — or made-up information — as well as misinformation are so common with large language models. These tools are simply creating sentences that look like other sentences they have seen in their training data. Inaccuracies, irrelevant information, debunked facts, false equivalencies — all of it — will show up in generated language if it exists in the training language. Many AI experts even think hallucinations are inevitable.

And yet, you can mitigate them. In fact, today’s large language models hallucinate less often than their predecessors, as shown in this inventive hallucination “leader board.” In addition, both data scientists and users have several solutions for reducing them.

Solution #1: AI Content prompting

Anyone who has tried an AI app is familiar with prompting. Basically, you tell the tool what you want to write and sometimes how you want to write it. There are simple prompts, such as, “List the advantage of using AI to write blog posts.”

Prompts can also be more sophisticated. For example, you can input a sample paragraph or page of text written according to your firm’s rules and voice and prompt the content AI to generate subject lines or social copy or a new paragraph in the same voice and using the same style.

Prompts are a first-line method for setting rules that narrow the output from content AI. Keeping your prompts focused, direct, and specific limits the chances that the AI will generate off-brand and misinformed copy.

Organizations are also experimenting with a form of prompt engineering called retrieval augmented generation, or RAG. With RAG-enhanced prompts, users point the model to fulfill the prompt using a specific source of information, often one that is not part of the original training set.

RAG does not 100% prevent hallucinations, but it can help content experts catch inaccuracies because they know what content the AI used to come up with an answer.

For more guidance on prompting techniques, check out this piece for content marketers on writing AI prompts, or read about researcher Lance Elliot’s nine rules for composing prompts to limit hallucinations.

Solution #2: “Chain of thought” prompting

Consider how you would solve a math problem or give someone directions in an unfamiliar city with no street signs. You would probably break down the problem into multiple steps and solve for each, leveraging deductive reasoning to find your way to the answer.

Chain of thought prompting leverages a similar process of breaking down a reasoning problem into multiple steps. The goal is to prime the LLM to produce text that reflects something resembling a reasoning or common-sense thinking process.

Scientists have used chain of thought techniques to improve LLM performance on math problems as well as on more complex tasks, such as inference — which humans automatically do based on their contextual understanding of language. Experiments show that with chain of thought prompts, users can produce more accurate results from LLMs.

Some researchers are even working to create add-ons to LLMs with pre-written prompts and chain-of-thought prompts so that the average user doesn’t need to learn how to do them.

Solution #3: Fine-tuning content AI

Fine-tuning involves taking a pre-trained large language model and training it to fulfill a specific task in a specific field by exposing it to relevant data for that field and eliminating irrelevant data.

A fine-tuned data language model ideally has all the language recognition and generative fluency of the original but focuses on a more specific context for better results.

There are hundreds of examples of fine-tuning for tasks like legal writing, financial reports, tax information, and so on. By fine-tuning a model using writings on legal cases or tax returns and correcting inaccuracies in generated results, an organization can develop a new tool that can draft clever content with fewer hallucinations.

If it seems implausible that these government-driven or regulated fields would use such untested technology, consider the case of a Colombian judge who reportedly used ChatGPT to draft his decision brief (without fine-turning).

Solution #4: Specialized model development

Many view fine-tuning a pre-trained model as a faster and less expensive approach compared with building new models. It’s not the only way, though. With enough budget, researchers and technology providers can also leverage the techniques of transformer models to develop specialized language models for specific domains or tasks.

For example, a group of researchers working at the University of Florida and in partnership with Nvidia, an AI technology provider, developed a specialized health-focused large language model to evaluate and analyze language data in the electronic health records used by hospitals and clinical practices.

The result was GatorTron, reportedly the largest-known LLM designed to evaluate the content in clinical records. The team has already developed a related model based on synthetic data, which alleviates privacy worries from using AI content based on personal medical records.

A recent experiment using the model to produce doctor’s notes resulted in AI-generated content that human readers could not identify as such 50% of the time.

Solution #5: Add-on functionality

Generating content is often part of a larger workflow within the business. Instead of stopping with the content, some developers are adding functionality on top of the content for greater value-add.

For example, researchers are trying to develop prompting add-ons so that everyday users don’t have to learn how to prompt well.

That’s just one example. Another comes from Jasper, whose Jasper for Business enhancements are a clear bid for enterprise-level contracts. These include a user interface that lets users define and apply their organization’s “brand voice” to all the copy they create. Jasper has also developed bots that allow users to use Jasper inside enterprise applications that require text.

Another solution provider called ABtesting.ai layers web A/B testing capabilities on top of language generation to test different variants of web copy and CTAs to identify the highest performer.

Next steps for leveraging content AI

The techniques I’ve described so far are enhancements or workarounds of the foundational models. As the world of AI continues to evolve and innovate, however, researchers will build AI with abilities closer to real thinking and reasoning.

The Holy Grail of “artificial generation intelligence” (AGI) — a kind of meta-AI that can fulfill a variety of different computational tasks — is still alive and well. Others are exploring ways to enable AI to engage in abstraction and analogy.

The message for humans whose life and passion is good content creation: AI is going to keep getting smarter. But we can “get smarter,” too.

I don’t mean that human creators try to beat an AI at the kind of tasks that require massive computing power. But for the time being, the AI needs prompts and inputs. Think of those as the core ideas about what to write. And even when a content AI surfaces something new and original, it still needs humans who recognize its value and elevate it as a priority. In other words, innovation and imagination remain firmly in human hands. The more time we spend using those skills, the wider our lead.

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