Large Language Models: The AI Wizards You Didn’t Know You Needed

Alright, let’s talk about Large Language Models (LLMs). They sound like something a sci-fi villain would use to take over the world, but they’re actually the secret sauce behind all those AI tools you’ve been casually asking to write your emails, summarize long reports, or translate your favorite song lyrics into Klingon (no judgment).

If you’ve dabbled with ChatGPT, Claude, or Microsoft Copilot, you’ve already met an LLM. Let’s break down what these AI brainiacs are, why they’re useful, and how not to let them write your press releases without a little adult supervision.

WHAT ARE LLMS, AND SHOULD YOU BE CONCERNED?

A Large Language Model is basically an AI built to understand, process, and generate human-like text. Imagine giving a really curious intern access to every book, article, and Reddit thread ever published—now you’ve got an LLM. They analyze all that data and learn how to piece together coherent responses to whatever you throw their way.

What can they do? Pretty much everything you’ve ever dreamed of outsourcing:

• Writing content like a caffeinated novelist.
• Condensing the 90-slide PowerPoint, you avoided into a digestible summary.
• Figuring out the vibe of social media posts (yes, Karen, it’s sarcasm).
• Translating your work into 27 languages because why not?

It’s the AI equivalent of a Swiss Army knife, except you don’t have to worry about cutting yourself.

WHY PR PROS SHOULD CARE

LLMs are like having an assistant who works 24/7, never complains, and has an uncanny ability to draft an email faster than you can say “deadline.” For PR professionals, they’re a game-changer. But, like that one friend who swears they can parallel park in two moves, they sometimes oversell their accuracy.

THE GOOD STUFF

Let’s start with the perks. LLMs can save you hours of time drafting press releases, social posts, and blog articles. They’re also amazing at analyzing sentiment, so you’ll know if your latest campaign is landing as intended or if it’s about to become a meme for all the wrong reasons. And if you’re working internationally, they can handle translations so you don’t accidentally say something regrettable in French.

THE NOT-SO-GOOD STUFF

Here’s where things get tricky. LLMs sometimes deliver content that’s as accurate as your uncle’s fishing stories. And bias? Yeah, that’s a thing. These models pull from human-made data, which means all our quirks, stereotypes, and bad habits are baked right in. That’s why double-checking their work isn’t just a nice-to-have—it’s a must.

HOW TO USE LLMS WITHOUT ACCIDENTALLY TANKING YOUR BRAND

To make the most of LLMs without running into trouble, it’s essential to approach them with a mix of enthusiasm and caution. Here are three strategies to ensure these tools enhance your work without compromising your credibility:

1. Fact-Check Like Your Reputation Depends on It
   Because, well, it does. LLMs mean well, but they’re not exactly valedictorians when it comes to accuracy.
2. Review for Bias
   Run everything through a human lens before hitting publish. If something feels off, it probably is. Have someone else on the team give it a read too—it never hurts to have backup.
3. Stay in Control of Your Narrative
   AI can be a powerful tool, but you’re still the one steering the ship. Use it to enhance your work, not replace your instincts. Every piece of content should reflect your brand’s voice, values, and mission, not just whatever an algorithm spat out after digesting the internet.

By sticking to these principles, you can leverage the power of LLMs while safeguarding your brand and maintaining trust with your audience.

MAKING AI WORK FOR YOU

LLMs are here, and they’re not going anywhere. They’re brilliant, occasionally clumsy, and absolutely worth having in your corner if you know how to use them wisely. So go ahead, embrace the magic of AI—but remember, you’re the wizard.

If you have a topic you’d like us to cover next, let us know. Preferably something less existential than whether robots are coming for our jobs. (Spoiler: they’re not… yet.)