What is RAG, and Why Should Marketers Care?

"What is RAG and why should I even bother? There are so many acronyms, I can't even keep track anymore." I can hear some of you saying this already.

Let's break it down in the simplest way possible: RAG (Retrieval-Augmented Generation) is a fundamental principle that shapes how we obtain information daily, often without even realizing it. It's transforming the way customers search and how brands engage with them at every stage of the journey, from discovery to conversion.

Let's make it simple: RAG combines two key things to make AI smarter.

1. Retrieval: It pulls relevant, accurate information from reliable sources like knowledge graphs, websites, or reviews.

2. Augmented generation: Using this data, the AI crafts responses that feel conversational and are grounded in facts.

Imagine RAG as an efficient assistant that enhances responses by retrieving real, relevant information before generating an answer. Instead of guessing or making things up, it grounds its responses in authoritative data to maintain accuracy and relevance. For example, if a customer asks a restaurant's AI chat assistant, "Do you have gluten-free options?" RAG retrieves information from the restaurant's menu, FAQs, and customer reviews to generate a response like, "Yes! We offer several gluten-free options, including salads and sandwiches with gluten-free bread."

Pro tip: A critical part of this process is how well the data is stored and organized. This is where a knowledge graph comes into play. By centralizing structured data (like store hours) and unstructured data (like customer reviews), a knowledge graph makes sure AI tools can access the information they need to deliver precise, contextually relevant answers. Without a solid data foundation, RAG can't perform at its full potential.

RAG is increasingly becoming part of every touchpoint in the customer journey. RAG could arguably be viewed as one of the biggest shifts to impact a customer journey for years, certainly since omnichannel marketing automation — and it's showing up everywhere:

• Discovery: AI-driven search results summarize relevant information quickly, helping customers find brands and products they might not have known about. For example, generative AI tools can surface lesser-known options by highlighting product features or benefits tied to the user's query.

• Consideration: RAG enhances product exploration by pulling detailed descriptions, reviews, and comparisons, giving customers the confidence to evaluate options. Instead of a list of links, customers receive curated insights, making it easier to evaluate their options.

• Decision: By retrieving real-time availability, pricing, or location details, RAG simplifies the decision-making process. Customers can act faster, whether it's visiting a store or completing an online purchase.

• Post-purchase support: AI tools powered by RAG provide fast, accurate responses to customer service queries, such as return policies or troubleshooting. This builds trust and loyalty, turning one-time buyers into repeat customers.

Pro tip: At every stage, the quality of the data that RAG retrieves is critical. Tools like knowledge graphs provide the structure and connections needed to make sure the AI is pulling accurate, reliable information, enhancing the customer experience across the board.

1. Understand the importance of data organization: Explore how tools like knowledge graphs can centralize and optimize your brand's information to improve AI retrieval and enhance customer engagement.

2. Embrace AI tools: Look into solutions that integrate RAG capabilities, such as advanced site search platforms, to enhance your customer experience.

3. Rethink SEO: Shift your strategy to prioritize relevance and depth. Create content that aligns with conversational queries and real-time information needs.

This isn't just hype. RAG aiding the shift to conversational search. It's making the customer journey smoother and more intuitive. By delivering smarter, faster, and more accurate answers, it's setting a new standard for customer experience.

Marketers who adapt to RAG now will be ready to meet the demands of tomorrow's search. And with tools like knowledge graphs powering this evolution, the brands that prioritize clean, organized data will lead the way.

Keep reading: The Role of a Knowledge Graph in RAG Success