Prompting, Fine-Tuning, RAG And AI Agents For Future Marketing

Prof. Aleks Farseev is an entrepreneur, keynote speaker and CEO of SOMIN, a communications and marketing strategy analysis AI platform.

Large language models, widely known as LLMs, have transformed digital marketing, offering unprecedented capabilities to automate, optimize and personalize strategies. However, businesses must choose the right approach based on their scale, resources and strategic goals. This article explores four key methods—prompting LLMs, building retrieval-augmented generation (RAG) systems, fine-tuning LLMs and developing AI agents—and evaluates their role in shaping the future of marketing.

The Role Of LLM Prompting In Marketing

For marketers new to AI, prompting LLMs is the easiest way to generate ad copy, blog posts and social media content. This method is especially useful for startups and small businesses building their online presence.

However, LLMs have limitations—they lack real-time data access, requiring human oversight for fact-checking. They also serve as ideation tools rather than execution engines, meaning they can assist in content creation but cannot autonomously run campaigns.

If your AI-generated ad copy feels repetitive or uninspired, it’s likely due to LLMs relying on a static knowledge base. To improve results, marketers can provide structured inputs, like brand guidelines, marketing frameworks (e.g., AIDA), historical campaign data, or website content. But for a more powerful solution, stepping into RAG-based AI is the next logical step.

RAG: The Data-Driven Marketing Engine

For businesses needing real-time, data-driven content, RAG systems offer a key advantage by integrating live data retrieval with AI-generated responses. This enhances market research, competitor analysis and automated reporting with up-to-date insights. Unlike standard LLMs, RAG fetches the latest external data, ensuring greater accuracy—especially useful for multinational corporations and marketing agencies adapting to regional trends and competition.

For example, when developing a recent product, our focus was on teaching LLMs to collect the most recent competitor content and categorize it into relevant marketing concepts that are contextually significant. Instead of relying on historical static data, RAG ensures that insights remain timely and actionable. Consider political advertising: An ad referencing Donald Trump might be highly relevant during the 2025 U.S. elections but would have been irrelevant before his presidency when the LLM was originally trained. Similarly, in retail, Christmas-themed advertisements perform well in December but may not resonate in January when the focus shifts to the Chinese New Year—a key seasonal trend among competitors. In marketing, this concept is known as moment of truth (MOT) planning, which involves identifying key moments when consumers, based on their mindset and behavior, are most likely to make a purchase decision.

This is where real-time RAG becomes crucial, dynamically adjusting marketing strategies based on shifting trends and consumer interests. By retrieving the latest content and feeding it into the LLM, businesses can generate meaningful marketing frameworks, such as Personas and Tensions. When combined with established marketing theories like Nudge Theory—static knowledge that remains relevant over time—and audience data, RAG enables highly specific and actionable recommendations for localized marketing strategies.

Best Practices And Key Considerations

Talking about RAG implementation, to maximize the effectiveness of RAG, in my opinion, businesses should focus on data quality, retrieval precision and contextual relevance. Ensuring retrieved information is accurate, up to date and contextually aligned with the user’s query prevents misinformation and improves trust. Additionally, combining RAG with prompt engineering helps optimize query structuring, improving retrieval efficiency. Regularly evaluating retrieved sources also mitigates the risk of low-quality or biased data influencing responses.

One more hurdle is the RAG deployment. Particularly, deploying RAGs in production environments requires technical expertise, infrastructure scalability and governance mechanisms. Ensuring retrieval latency does not slow down real-time applications is crucial, especially in high-speed industries like marketing and finance. Furthermore, businesses must balance computational costs, as frequent API calls and real-time data processing can strain resources.

As you might guess, RAG’s effectiveness relies on data quality and diversity and serves as a tool for ideation, not execution. Integrating RAG into marketing enables dynamic, personalized content, enhancing audience engagement. However, for highly specific brand identity or hyper-personalized content, LLM fine-tuning may still be necessary.

Fine-Tuning LLMs For Brand Consistency

Fine-tuning LLMs on proprietary data allows businesses to achieve superior brand alignment. By training AI on historical marketing campaigns, customer interactions and brand voice guidelines, companies can ensure AI-generated content remains precise, consistent and aligned with their identity. These organizations can overcome the limitations of generic AI models by leveraging fine-tuned systems to generate real-time marketing content and hyper-personalized advertising.

To illustrate, try asking your favorite LLM to generate an ad banner for McDonald’s. You’ll likely notice that the result is relatively polished, with logos appearing less distorted than if you were to request an ad for a lesser-known brand. Why is that? Because McDonald’s has produced vast amounts of digital content that the LLM has encountered during training, it can generalize and reproduce brand-consistent material with higher accuracy.

But how about the vast majority of the businesses, those companies that do not have the resources to fine-tune LLM but would like to run their marketing in a state-of-the-art fashion?

AI Agents: The Future Of Autonomous Marketing

Unlike static LLMs or RAG-based systems, AI agents represent a significant leap forward in marketing automation. Unlike traditional AI models that focus solely on recommendations, AI agents come equipped with toolsets that enable them to interact directly with ad platforms like Meta. This expanded capability allows AI agents to execute tasks autonomously, making them far more actionable than recommender systems.

The next generation of AI-driven marketing solutions will be able to launch ad campaigns, optimize real-time bidding and dynamically adjust content based on audience engagement—all with a little human intervention, which would be a drastic help for the business climbing from 0 to 1 in their marketing journey.

Choosing The Right AI Strategy

The future of marketing AI isn’t about the most advanced model but the right tool for the task. Small businesses can improve their online presence with LLM prompting or AI agents, while global corporations may benefit from RAG for research and LLM fine-tuning for brand consistency. AI isn’t replacing marketers—it’s empowering them, enhancing creativity and strategic decision making rather than diminishing it.

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