Fine-Tuning, RAG And AI Agents For Future Marketing

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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.

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.

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.

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.