Digital marketing is riding the AI wave, and it’s not just about flashy buzzwords. With the rise of large language models, businesses—big and small—now have a suite of tools to automate, optimise, and personalise their marketing efforts. But with multiple AI strategies available, which one really fits your business?
Let’s break down four key approaches shaking up the scene: LLM prompting, Retrieval-Augmented Generation (RAG), fine-tuning, and AI agents.
LLM prompting: Your fast track to creative content
For startups and small businesses, LLM prompting is the easiest way to dive into AI-powered content creation. Whether it’s ad copy, blog posts, or social media updates, a well-crafted prompt can unlock a flood of creative ideas.
However, don’t expect a plug-and-play solution. LLMs are great for ideation, but they’re working off a static knowledge base—meaning they might miss out on the latest trends and require a human touch for fact-checking and context.
Pro tip: Feed your LLM with structured inputs like brand guidelines, historical campaign data, or key marketing frameworks to get more tailored results.
RAG: Real-time data meets intelligent marketing
For marketers who live by the numbers, RAG systems bring a whole new level of dynamism. Unlike standard LLMs, RAG marries AI-generated content with live data, ensuring that your marketing insights are both timely and relevant.
Imagine running a campaign that adapts in real-time to shifting market trends—whether it’s seasonal events or regional consumer behaviour—RAG has you covered.
Consider this: a campaign referencing a trending political figure or seasonal event will resonate better if it pulls the latest data rather than relying solely on historical information. In essence, RAG transforms your marketing strategy into a data-driven engine that keeps pace with a rapidly evolving landscape.
Pro tip: Try playing with free-to-try RAGs for marketing, like DAPTA AI or SOMONITOR to understand if they could bring significantly more in-depth responses in your daily marketing routines than context-aware LLM prompting.
Also Read: How AI and automation are shaping the future of work
Fine-tuning: Locking in your brand’s unique voice
Generic AI can only take you so far. For brands that demand consistency, fine-tuning LLMs on proprietary data is the way to go. By training your AI on past campaigns, customer interactions, and specific brand guidelines, you ensure that every piece of content stays true to your identity. Think of it like customising a suit—the better the fit, the more confident you feel.
Even global giants like McDonald’s benefit from this approach, as their vast digital footprint allows their AI to churn out highly polished, brand-consistent material. For many businesses, though, finding the right balance between cost and customization remains key.
Pro tip: Open your favourite LLM and try prompting, “Generate a square banner for a Coca-Cola ad.” You’ll likely get a well-designed and polished banner. Now, try the same with “Generate a square banner for a Gojek ad.” Since Gojek is a more localised brand and less familiar to the LLM, the result may be significantly weaker.
Because the LLM has much less knowledge about Gojek compared to Coca-Cola, companies like Gojek could benefit greatly from fine-tuning their AI for ad creative generation. By training the model with more Gojek-specific content, they can elevate their AI-generated creatives to the same quality level as Coca-Cola’s, ensuring brand consistency and stronger engagement.
AI agents: The autonomous marketing mavericks
Moving beyond content generation, AI agents are poised to revolutionise marketing execution. These aren’t just recommendation engines—they’re proactive tools that can interact with platforms (like Meta) to launch campaigns, optimise bidding strategies, and adjust content in real time based on engagement metrics.
For companies scaling up their marketing efforts, AI agents offer a powerful way to automate routine tasks while still leaving room for strategic human oversight. This leap towards autonomous marketing can be a game changer for businesses looking to transition from zero to one.
Pro tip: Today, many AI vendors label their solutions as ‘Agents,’ but don’t be misled. Ask yourself: Does this software truly interact with other systems to complete a task from start to finish? Does it engage in multi-step reasoning across several interactions, or does it simply generate a single output? If the answer is NO, then it’s most likely a RAG system, not a true AI agent.
Also Read: AI agents redefine art: Unlocking boundless creative possibilities in a new digital era
Choosing the right AI strategy for your business
There’s no one-size-fits-all when it comes to AI in marketing. Small businesses might start with LLM prompting or dip into AI agents to get an edge, while larger enterprises can leverage RAG for its real-time insights and fine-tuning for brand consistency.
Ultimately, AI isn’t here to replace human marketers—it’s here to amplify creativity and strategic decision-making, letting you focus on the big picture while the tech handles the grunt work.
In today’s fast-evolving digital landscape, the key is to match your AI strategy with your business’s scale, resources, and goals. Whether you’re refining your ad copy or automating entire campaigns, the future of marketing is all about using the right tool for the job.
—
Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.
Join us on Instagram, Facebook, X, and LinkedIn to stay connected.
Image courtesy: Canva Pro
The post LLM prompting, fine-tuning, RAG, or AI agents: Which AI is better for marketing? appeared first on e27.