RAG: What Every Brand Marketer Needs to Know

Using proprietary data in AI workflows has the potential to transform brand marketing, but there aren’t any one-size-fits-all solutions. To make matters worse, the field is filled with jargon and hype. I can’t do much about the hype, but I can arm you with some high-level concepts to facilitate your AI-focused discussions. With that in mind, here’s a brief overview of Retrieval-Augmented Generation (RAG), one of the most popular ways to incorporate your proprietary data into generative AI workflows.

What is a RAG Database?

RAG databases are systems that enhance the capabilities of Large Language Models (LLMs) by integrating external data sources (your proprietary data or third-party data, such as cultural insights or trend data) to improve the relevance and accuracy of generated responses. In essence, RAG works by breaking data into chunks—small amounts of data such as a sentence, paragraph, or short answer to a question—that are retrieved by the LLM when needed. The ability to use proprietary data—whether customer insights, campaign results, or other sensitive information—can provide a competitive edge by creating more personalized and effective messaging.

Security

Using proprietary data comes with inherent risks. It’s important to recognize that AI data security is a cross-functional effort involving several key disciplines. While the SecOps (Security Operations) team handles threat detection, monitoring, and response, they collaborate with the Data Governance team to ensure proper management and compliance with regulations. Legal and Compliance teams enforce policies around proprietary data use, while Data Science and Engineering teams ensure security is integrated into the design and maintenance of systems like RAG databases. Additionally, the IT/Infrastructure team oversees access control and network security, and Risk Management and Data Privacy Officers play crucial roles in assessing and mitigating risks to sensitive data. As your teams prepare your data for use in RAG systems, ensuring proper controls are in place at every step is essential to maintaining both performance and security.

Understanding the Data Preparation Process for RAG

To properly use data with a RAG system, it must first be prepared. One key concept is chunking—breaking your data into smaller, manageable pieces that can be efficiently retrieved by the LLM. This is important because LLMs are designed to process information incrementally, not all at once. Proper chunking ensures that only the most relevant pieces of data are retrieved when needed.

Fine-tuning in the context of a RAG system involves further training the underlying language model to specialize in tasks relevant to your proprietary data. This process allows the RAG system to generate more precise and context-aware responses based on your specific business needs. Additionally, fine-tuning can optimize how the system retrieves external data, ensuring that the most relevant information is incorporated.

Finally, post-training techniques—such as adjusting the model’s weights and refining outputs—help optimize the system after the initial training. These techniques enhance both performance and security, as they allow for the refinement of how the model interacts with your proprietary data.

Common RAG Techniques: Matching Complexity with Value

While the fundamental process of preparing data for a RAG database remains consistent, there are several different methodologies, each with its own level of complexity, cost, and value. Here’s a brief overview.

Importantly, the industry is evolving so quickly that many of these approaches have slightly different names in the real world. Pay attention to the definitions, that’s where the differences become obvious and agreed upon:

Standard RAG is the simplest approach, breaking documents into chunks and retrieving them in real time. It’s cost-effective and works well for straightforward applications where response speed is important, with a relatively low security risk due to the simplicity of the data structures involved.

Feedback-Driven or Iterative RAG focuses on accuracy, running multiple passes to correct errors in the generated responses. While this results in higher precision, the complexity and cost increase due to the feedback loops required for error correction. This method is valuable when user satisfaction and precision are top priorities.

Two-Stage RAG (aka Speculative Decoding or Two-Stage Retrieval) uses a small, specialized model that generates initial drafts, and a larger model verifies the output. This parallel processing speeds up response times while maintaining high accuracy. The additional computational cost is justified when fast, precise responses are needed, making it ideal for dynamic marketing environments.

Multi-Source RAG integrates multiple data sources, improving response quality by drawing from a wider range of inputs. While this increases complexity, it adds significant value in scenarios where diverse data sets are required to create nuanced, comprehensive responses. The cost is moderate to high but justified by the richness of the output.

Adaptive RAG uses adaptive agents to adjust information retrieval strategies in real time, offering precision for more complex tasks. While highly valuable for real-time applications, Adaptive RAG comes with a higher complexity and cost due to the need for modular integration and real-time decision-making.

Self-Retrieval RAG allows the model to iteratively refine its responses by retrieving its own outputs for context. This ensures consistency and improves accuracy over time. It’s a medium-cost solution that offers long-term efficiency gains, especially when models need to become progressively smarter and more self-reliant.

Cost Considerations

The complexity of each RAG methodology directly impacts costs, not just financially but also in terms of time and technical expertise. For instance, Standard RAG is simple and cost-effective, suitable for basic retrieval tasks, while Adaptive and Multi-Source RAGs, which integrate multiple data sources or require real-time adaptability, increase both complexity and cost. If your goal is to enhance response quality with proprietary data while ensuring strict security, the higher cost of Iterative RAG or Multi-Source RAG may be justified. However, for simpler, faster solutions like proof of concept, Standard or Two-Stage RAG might be more appropriate.

Go Forth Unafraid

Your role isn’t to choose what RAG to use, but to ensure that the right people and vendors are making the correct decisions for your data. Ask about chunking, fine-tuning, and post-training. Enquiring about the range of RAG techniques will ensure meaningful conversations with your IT, data science, legal, and security teams as well as your suppliers. By doing so, you’ll be one step closer to leveraging your proprietary data with AI to drive growth, securely and effectively.

Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.