Key takeaways:
  • What are LLMs? Large language models (LLMs) are AI systems that generate human-like text using extensive training datasets but can sometimes produce inaccurate content.
  • How do LLMs improve content creation? LLMs speed up the writing process, personalize content efficiently, assist with writer's block, and repurpose original material.
  • What challenges do LLMs present? Writing for LLM optimization requires a shift in mindset and structure, and not all content types benefit from LLMs, as human voice and narrative are essential.
  • Do LLMs replace human writers? No, LLM-generated content necessitates human editing and insight to maintain brand voice and authenticity.
  • How can marketers balance AI and human creativity? Combining LLM efficiency with genuine storytelling fosters impactful content that resonates with audiences.

I've been a full-time writer for about 15 years, and I've experienced the landscape shifting slowly over that time. When I started writing as a freelance journalist, SEO was barely making its way onto the scene. Over time, it became more and more important for online work, and I've woven hundreds, if not thousands, of keywords into articles over the years. Seamlessly, I might add. 

But LLMs are changing the game for content creators and writers — for better and for worse. 

Let's dive into the nitty-gritty of how LLMs are changing content creation. 

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What are LLMs?

LLMs stand for large language models, advanced AI systems trained on large datasets of text sourced from the internet, books, articles, and any other words LLM companies like OpenAI or Anthropic can get their hands on. Through these datasets, LLMs learn how to generate human-like language. They can spit out thousands of words of content in mere minutes, and their deep learning on human writing means they can produce coherent, contextual, and creative language — fast. 

LLMs are becoming widespread and universally used, but keep in mind that they do have a few drawbacks. LLMs — like ChatGPT, Meta's Llama, and Claude AI — are fast, but they aren't always accurate. Sometimes they link to articles that don't exist, sometimes they hallucinate information entirely, and sometimes they use em dashes in every single sentence. 

LLMs also use a ton of energy when they're being trained on large datasets, and they use more energy than a simple Google search every time you ask one a question. The amount of energy used remains unclear because many companies don't report how much energy they're consuming. 

The World Economic Forum reported last year that “a typical AI data centre, according to the International Energy Agency (IEA), uses as much power as 100,000 households right now, but the largest centres currently being constructed will consume 20 times that amount.” The amount of energy used per LLM query is a moving target, sometimes estimated as high as 10 times that of a Google query, although that energy usage may become more efficient over time. That's something I keep in mind when I use an LLM — but for marketers, LLMs are becoming almost unavoidable. 

How are LLMs improving content creation? 

LLMs are changing content creation in a wide range of ways. Let's go through a few ways LLMs improve content creation: 

  1. LLMs make tedious content creation work faster and more painless. Take Google ad creation as an example. Did you know that at the time of this writing, creating Google responsive search ads requires writing 15 headlines and four descriptions for each keyword? There's nothing that will break your brain faster than trying to fill in a spreadsheet with 15 similar-but-different headlines. An LLM can spit out variations in seconds that can be surprisingly well written, depending on how well you write the prompt. Editing and adjusting still require a human writer, but an LLM will cut down on the total time on the task. 
  2. LLMs can analyze customer data and help you better personalize content. Marketers know that content personalization sets their content above the rest. LLMs can analyze ideal customer profile (ICP) data far faster than humans could, pulling out information about specific audience segments. You can feed an LLM a set of generic emails along with core details about particular audiences, and within seconds, generate personalized content that's more likely to resonate. 
  3. LLMs really do help eliminate writer's block. Staring at a blank page can be daunting for a writer. I often ask our internal Wrike AI portal a few questions to get thoughts flowing about a topic, helping to jump-start the process quickly. LLMs can also help generate outlines that can keep a writer from getting stuck between sections. 
  4. LLMs can repurpose original content quickly. Once you've written the blog post, case study, or eBook, you can put that copy into an LLM and ask it to repackage it into promotional assets like social media posts, email copy, and ad copy. Here at Wrike, this type of LLM use saves us considerable time, even when we tweak or rewrite certain elements to make the new copy align well. 

How are LLMs making content creation harder? 

Now let's talk a little about how LLMs make work a little tougher, for writers in particular. 

  1. Writing for LLM optimization requires a complete mindset shift. Whereas writing for SEO has changed incrementally over the last decade, writing for LLM scanning has fundamentally changed the structure and inclusions for a lot of writing. What LLMs are looking for in blog posts is much different from a book layout or newspaper article, for example. When writing for LLMs, you'll be including more scannable bullet points, highlighted definitions, and headlines written as questions with immediate answers. 
  2. Not all writing is designed to be optimized for LLMs. While it's tempting to try to optimize everything for LLMs in this era where they seem to be running the show, not all content is designed to be scanned by LLMs. For instance, thought leadership and brand foundation content still needs to hold tightly to the brand's voice and tone, and should be written so prospective customers or industry peers can gain an understanding of the brand's perspective through a narrative. 
  3. LLM-generated content still needs the human element. Our Wrike CMO, Christine Royston, gave a keynote at a recent conference about this exact topic. She translated her keynote into a blog post, and this is one of my favorite quotes: 
Author Avatar

Your team's ability to read between the lines, to connect emotion with action, to embody your brand's identity in everything they create is a clear human advantage. And it's an edge we're determined to protect.

Christine Royston, CMO at Wrike

LLM-generated content still needs human writers and content creators to edit, adjust, iterate, and fact-check. Without the human element, assets will ring hollow, and your brand voice will dissipate. 

LLMs are transforming content creation by making task completion faster and more efficient, but they also require writers to adapt and maintain a strong human touch. As LLMs continue to reshape the creative landscape, the best results will continue to come from blending AI's capabilities with authentic storytelling and brand voice. Embracing both will keep your content relevant, impactful, and authentic. 

Hitting the sweet spot with Wrike AI and the human element

Here at Wrike, we really are determined to protect the human edge that keeps marketing assets authentic — while we continue to invest in our AI capabilities that will give marketers superpowers. We'd love for you to see that balance in action. Did you know you can try Wrike for free for two weeks? Give it a shot, and start creating your best content yet. 

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FAQs: 

What is a language model, and how is it applied to content creation?

A large language model (LLM) is an artificial intelligence system trained to understand and generate human language by predicting the next word or phrase in a sequence. When used for content creation, LLM technology helps streamline workflows, improve consistency, and increase productivity for writers and marketers.

What are some examples of content creation tasks that LLMs can assist with?

LLMs can assist with drafting blog posts, articles, newsletters, and social media content, brainstorming headlines, editing for clarity, and creating scripts for videos or webinars. They are also helpful for summarizing documents, personalizing marketing messages, translating content, and repurposing existing material for different audiences. 

Can you break down how large language models influence click-through rates on my content?

Large language models influence click-through rates (CTR) by helping craft more engaging headlines, personalized calls to action, and relevant content that resonates with target audiences. Their ability to analyze large datasets allows them to optimize language for higher engagement, making content more likely to attract clicks. LLMs can adapt and refine content strategies to boost CTR over time by continuously learning from user interactions and feedback. 

How do large language models source information from content? 

Large language models source information from content by analyzing vast amounts of publicly available text data during their training. This helps them to learn patterns, facts, and language usage. They do not access external sources or databases in real time; instead, they generate responses based on the knowledge encoded in their training data up to a certain cutoff date. When you ask an LLM a question, LLMs use this learned information to produce relevant, coherent, and contextually appropriate content.