The distinctions between generative AI and large language models for marketers.

I wanted to share a conversation I had with a client this week regarding the distinctions between generative AI and large language models and when each type of AI best serves marketing objectives. They wanted to know how these tools could elevate their marketing efforts and which methods would drive the most value.? I figured I would share my recommendation, even though I didn’t get the job, I thought I would share with my network some useful insights.

First Understanding AI: Generative AI vs. LLMs

Let’s break down the basics. Generative AI is great for creating new content or ideas, like social media posts, emails, or even ads that reflect the brand's tone and personality. It’s like having a copywriter who can churn out endless drafts tailored to specific needs and audiences. Large language models (LLMs), on the other hand, are the underlying brains that power many generative AI applications. They’re trained on massive datasets, allowing them to understand context and language nuances. When you’re looking for an AI that can interact conversationally—like a chatbot for customer service or a virtual assistant on your website—that’s where LLMs shine.?

Choosing the Right AI Method for Marketing Goals

The next question they had was, "When should we use generative AI versus LLMs for our goals?" I explained that it boils down to what they’re aiming to achieve in each part of their marketing strategy.

1. Content Generation for Scale: For scalable content production, like creating product descriptions, blog posts, or social media updates, generative AI is fantastic. It can follow brand guidelines, adjust tone based on audience, and generate content quickly for campaigns. It’s also excellent for A/B testing, helping the team identify what messaging resonates most with specific demographics without endless revisions.

2. Customer Interaction and Engagement: When they want to improve customer interaction, especially in areas like customer service or personalized product recommendations, LLM-powered chatbots or virtual assistants are the way to go. These models have the ability to interpret questions, deliver relevant answers, and even manage complex customer requests. This is particularly useful for large audiences with diverse inquiries, ensuring each interaction feels personal and responsive.

3. Data Analysis and Insight Generation: I also recommended leveraging LLMs when they need insights from large volumes of text, such as customer feedback or market research reports. These models can sift through data, identify patterns, and summarize findings that could inform future campaigns or product development.

4. Personalized Campaigns: Both generative AI and LLMs can work together here. Generative AI can handle the content creation, while an LLM personalizes responses based on the customer’s past interactions, location, or preferences, like adjusting email offers based on what the customer recently browsed on the site.

Implementing AI Effectively in Marketing Execution

At the end of our conversation, I emphasized that no AI is one-size-fits-all. It’s about choosing tools that align with specific parts of the customer journey and marketing objectives. I suggested they start with smaller use cases, like using a generative AI tool for email campaigns or an LLM chatbot for website FAQs. As they see results, they can expand into more areas, refining and adjusting based on what’s working.

The client left the meeting with a clearer understanding of how to strategically approach AI in their marketing—not just using it for novelty but applying it in ways that support their goals and resonate with their customers.

I hope this helps others.

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?If you're interested in learning more about how to improve your marketing transformation, feel free to contact me for further insights and recommendations from my experience working with and leading some of the most iconic companies.

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